p_table <- function(tab_data, ...) {
  tab_data_2 <- deparse(substitute(tab_data))
  
  table_p <- do.call(CreateTableOne, 
                     list(data = as.name(tab_data_2), includeNA = TRUE, ...))
  table_p_out <- print(table_p,
                       showAllLevels = TRUE,
                       printToggle = FALSE)
  kable(table_p_out,
        align = "c")
}
uni_var <- function(test_var, data_imp) {
                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")
        
        f <- as.formula(paste("Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)",
                              as.name(test_var),
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))
        km_fit <- do.call("survfit", list(formula = f, data = as.name(data_imp_2)))
        print(km_fit)
        cat("\n")
        print(summary(km_fit, times = c(12, 24, 36, 48, 60, 120)))
        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Univariable Cox Proportional Hazard Model for: ", test_var)
        cat("\n")
        cat("\n")
        n_levels <- nlevels(data_imp[[test_var]])
        if(n_levels == 1){
                print("Only one level, no Cox model performed")
                cat("\n")
        } else {
                cox_fit <- do.call("coxph", list(formula = f, data = as.name(data_imp_2)))
                print(summary(cox_fit))
                cat("\n")
                
                do.call("ggforest",
                         list(model = cox_fit, data = as.name(data_imp_2)))
        }
        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Unadjusted Kaplan Meier Overall Survival Curve for: ", test_var)
        p <- do.call("ggsurvplot",
                     list(fit = km_fit, data = as.name(data_imp_2),
                          palette = "jco", censor = FALSE, legend = "right",
                          linetype = "strata", xlab = "Time (Months)"))
        print(p)
}
col.width <- c(37, 10, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 4, 4, 1, 4, 1, 1,
               1, 3, 2, 2, 8, 2, 5, 5, 5, 4, 5, 5, 5,4, 2, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 3,
               3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 8,
               8, 8, 2, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 2, 2, 5, 2, 5, 3, 1, 3, 1, 8, 8, 2, 8,
               2, 8, 2, 2, 1, 8, 1, 1, 1, 1, 1, 8, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 3, 1, 1,
               1, 1, 1, 1, 1, 1, 1)
col.names.abr <- c("PUF_CASE_ID", "PUF_FACILITY_ID", "FACILITY_TYPE_CD", "FACILITY_LOCATION_CD",
                   "AGE", "SEX", "RACE", "SPANISH_HISPANIC_ORIGIN", "INSURANCE_STATUS",
                   "MED_INC_QUAR_00", "NO_HSD_QUAR_00", "UR_CD_03", "MED_INC_QUAR_12", "NO_HSD_QUAR_12",
                   "UR_CD_13", "CROWFLY", "CDCC_TOTAL_BEST", "SEQUENCE_NUMBER", "CLASS_OF_CASE",
                   "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "LATERALITY", "HISTOLOGY", "BEHAVIOR", "GRADE",
                   "DIAGNOSTIC_CONFIRMATION", "TUMOR_SIZE", "REGIONAL_NODES_POSITIVE",
                   "REGIONAL_NODES_EXAMINED", "DX_STAGING_PROC_DAYS", "RX_SUMM_DXSTG_PROC", "TNM_CLIN_T",
                   "TNM_CLIN_N", "TNM_CLIN_M", "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                   "TNM_PATH_STAGE_GROUP", "TNM_EDITION_NUMBER", "ANALYTIC_STAGE_GROUP", "CS_METS_AT_DX",
                   "CS_METS_EVAL", "CS_EXTENSION", "CS_TUMOR_SIZEEXT_EVAL", "CS_METS_DX_BONE", "CS_METS_DX_BRAIN",
                   "CS_METS_DX_LIVER", "CS_METS_DX_LUNG", "LYMPH_VASCULAR_INVASION", "CS_SITESPECIFIC_FACTOR_1",
                   "CS_SITESPECIFIC_FACTOR_2", "CS_SITESPECIFIC_FACTOR_3", "CS_SITESPECIFIC_FACTOR_4",
                   "CS_SITESPECIFIC_FACTOR_5", "CS_SITESPECIFIC_FACTOR_6", "CS_SITESPECIFIC_FACTOR_7",
                   "CS_SITESPECIFIC_FACTOR_8", "CS_SITESPECIFIC_FACTOR_9", "CS_SITESPECIFIC_FACTOR_10",
                   "CS_SITESPECIFIC_FACTOR_11", "CS_SITESPECIFIC_FACTOR_12", "CS_SITESPECIFIC_FACTOR_13",
                   "CS_SITESPECIFIC_FACTOR_14", "CS_SITESPECIFIC_FACTOR_15", "CS_SITESPECIFIC_FACTOR_16",
                   "CS_SITESPECIFIC_FACTOR_17", "CS_SITESPECIFIC_FACTOR_18", "CS_SITESPECIFIC_FACTOR_19",
                   "CS_SITESPECIFIC_FACTOR_20", "CS_SITESPECIFIC_FACTOR_21", "CS_SITESPECIFIC_FACTOR_22",
                   "CS_SITESPECIFIC_FACTOR_23", "CS_SITESPECIFIC_FACTOR_24", "CS_SITESPECIFIC_FACTOR_25",
                   "CS_VERSION_LATEST", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS", "DX_DEFSURG_STARTED_DAYS",
                   "RX_SUMM_SURG_PRIM_SITE", "RX_HOSP_SURG_APPR_2010", "RX_SUMM_SURGICAL_MARGINS",
                   "RX_SUMM_SCOPE_REG_LN_SUR", "RX_SUMM_SURG_OTH_REGDIS", "SURG_DISCHARGE_DAYS", "READM_HOSP_30_DAYS",
                   "REASON_FOR_NO_SURGERY", "DX_RAD_STARTED_DAYS", "RX_SUMM_RADIATION", "RAD_LOCATION_OF_RX",
                   "RAD_TREAT_VOL", "RAD_REGIONAL_RX_MODALITY", "RAD_REGIONAL_DOSE_CGY", "RAD_BOOST_RX_MODALITY",
                   "RAD_BOOST_DOSE_CGY", "RAD_NUM_TREAT_VOL", "RX_SUMM_SURGRAD_SEQ", "RAD_ELAPSED_RX_DAYS",
                   "REASON_FOR_NO_RADIATION", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", "RX_SUMM_CHEMO",
                   "DX_HORMONE_STARTED_DAYS", "RX_SUMM_HORMONE", "DX_IMMUNO_STARTED_DAYS", "RX_SUMM_IMMUNOTHERAPY",
                   "RX_SUMM_TRNSPLNT_ENDO", "RX_SUMM_SYSTEMIC_SUR_SEQ", "DX_OTHER_STARTED_DAYS", "RX_SUMM_OTHER",
                   "PALLIATIVE_CARE", "RX_SUMM_TREATMENT_STATUS", "PUF_30_DAY_MORT_CD", "PUF_90_DAY_MORT_CD",
                   "DX_LASTCONTACT_DEATH_MONTHS", "PUF_VITAL_STATUS", "RX_HOSP_SURG_PRIM_SITE", "RX_HOSP_CHEMO",
                   "RX_HOSP_IMMUNOTHERAPY", "RX_HOSP_HORMONE", "RX_HOSP_OTHER", "PUF_MULT_SOURCE", "REFERENCE_DATE_FLAG",
                   "RX_SUMM_SCOPE_REG_LN_2012", "RX_HOSP_DXSTG_PROC", "PALLIATIVE_CARE_HOSP", "TUMOR_SIZE_SUMMARY",
                   "METS_AT_DX_OTHER", "METS_AT_DX_DISTANT_LN", "METS_AT_DX_BONE", "METS_AT_DX_BRAIN",
                   "METS_AT_DX_LIVER", "METS_AT_DX_LUNG", "NO_HSD_QUAR_16", "MED_INC_QUAR_16", "MEDICAID_EXPN_CODE")
#Read in data for each subsite
lip <- read_fwf('NCDBPUF_Lip.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
melanoma <- read_fwf('NCDBPUF_Melanoma.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
                       
skin <- read_fwf('NCDBPUF_OtSkin.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
hodgextr <- read_fwf('NCDBPUF_HodgExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
hodgndal <- read_fwf('NCDBPUF_HodgNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
NHLndal <- read_fwf('NCDBPUF_NHLNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
breast <-  read_fwf('NCDBPUF_Breast.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
vulva <-  read_fwf('NCDBPUF_Vulva.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
vagina <-  read_fwf('NCDBPUF_Vagina.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
penis <-  read_fwf('NCDBPUF_Penis.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
#Combine data for all subsites
dat <- bind_rows(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, 
                 vulva, vagina, penis)
rm(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, vulva, vagina, penis)
prim_site_text <- data_frame(PRIMARY_SITE = 
                                 #lip
                               c("C000", "C001", "C002", "C003", "C004", "C005",
                                 "C006", "C008", "C009",
                                 
                                 #skin/melanoma
                                 "C440", "C441", "C442", "C443", "C444", "C445",
                                 "C446", "C447", "C448", "C449",
                                 
                                 #breast - nipple
                                 "C500",
                                 
                                 #vagina/vulva
                                 "C510", "C511", "C512", "C518", "C519", "C529",
                                 
                                 #penis
                                 "C600", "C601", "C602", "C608", "C609", "C639"
                                 ),                
SITE_TEXT = c(
  #lip 
"C00.0 External Lip: Upper NOS",
"C00.1 External Lip: Lower NOS",
"C00.2  External Lip: NOS",
"C00.3 Lip: Upper Mucosa", 
"C00.4 Lip: Lower Mucosa", 
"C00.5 Lip: Mucosa NOS",
"C00.6 Lip: Commissure", 
"C00.8 Lip: Overlapping", 
"C00.9 Lip, NOS",
 #skin
"C44.0 Skin of lip, NOS",
"C44.1 Eyelid",
"C44.2 External ear",
"C44.3 Skin of ear and unspecified parts of face",
"C44.4 Skin of scalp and neck",
"C44.5 Skin of trunk",
"C44.6 Skin of upper limb and shoulder",
"C44.7 Skin of lower limb and hip",
"C44.8 Overlapping lesion of skin",
"C44.9 Skin, NOS", 
#breast
"C50.0 Nipple",
#vulva/vagina
"C51.0 Labium majus",
"C51.1 Labium minus",
"C51.2 Clitoris",
"C51.8 Overlapping lesion of vulva",
"C51.9 Vulva, NOS",
"C52.9 Vagina, NOS",
#penis
"C60.0 Prepuce",
"C60.1 Glans penis",
"C60.2 Body of penis",
"C60.8 Overlapping lesion of penis",
"C60.9 Penis",
"C63.2 Scrotum, NOS"))
dat <- merge(dat, prim_site_text, by = "PRIMARY_SITE", all.x = TRUE) 
 
rm(prim_site_text)
# convert numeric variables from character class to numeric class
num_vars <- c("AGE", "CROWFLY", "TUMOR_SIZE", "DX_STAGING_PROC_DAYS", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
              "DX_DEFSURG_STARTED_DAYS", "SURG_DISCHARGE_DAYS", "DX_RAD_STARTED_DAYS",  "RAD_REGIONAL_DOSE_CGY",
              "RAD_BOOST_DOSE_CGY", "RAD_ELAPSED_RX_DAYS", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", 
              "DX_HORMONE_STARTED_DAYS", "DX_OTHER_STARTED_DAYS", "DX_LASTCONTACT_DEATH_MONTHS",
              "RAD_NUM_TREAT_VOL")
dat[num_vars] <- lapply(dat[num_vars], as.numeric)
# convert factor variables from character class to factor class
vars <- names(dat)
fact_vars <- vars[!(vars %in% num_vars)] # basically all of the non-numerics
dat[fact_vars] <- lapply(dat[fact_vars], as.character)
dat[fact_vars] <- lapply(dat[fact_vars], as.factor)
dat <- dat %>%
        mutate(FACILITY_TYPE_F = fct_recode(FACILITY_TYPE_CD,
                                            "Community Cancer Program" = "1",
                                            "Comprehensive Comm Ca Program" = "2",
                                            "Academic/Research Program" = "3",
                                            "Integrated Network Ca Program" = "4",
                                            "Other" = "9")) %>%
        mutate(FACILITY_LOCATION_F = fct_recode(FACILITY_LOCATION_CD,
                                            "New England" = "1",
                                            "Middle Atlantic" = "2",
                                            "South Atlantic" = "3",
                                            "East North Central" = "4",
                                            "East South Central" = "5",
                                            "West North Central" = "6",
                                            "West South Central" = "7",
                                            "Mountain" = "8",
                                            "Pacific" = "9",
                                            "out of US" = "0")) %>%
        mutate(FACILITY_GEOGRAPHY = fct_collapse(FACILITY_LOCATION_CD,
                                                 "Northeast" = c("1", "2"),
                                                 "South" = c("3", "7"),
                                                 "Midwest" = c("4", "5", "6"),
                                                 "West" = c("8", "9"))) %>%
        mutate(AGE_F = cut(AGE, c(0, 54, 64, 74, 100))) %>%
        mutate(AGE_40 = cut(AGE, c(0, 40, 100))) %>%
        mutate(SEX_F = fct_recode(SEX,
                                "Male" = "1",
                                "Female" = "2")) %>%
        mutate(RACE_F = fct_collapse(RACE,
                                "White" = c("01"),
                                "Black" = c("02"),
                                "Asian" = c("04", "05", "06", "07", "08", "10", "11", "12", "13", "14", "15",
                                            "16", "17", "20", "21", "22", "25", "26", "27", "28", "30", "31",
                                            "32", "96", "97"),
                                "Other/Unk" = c("03", "98", "99"))) %>%
        mutate(HISPANIC = fct_collapse(SPANISH_HISPANIC_ORIGIN,
                                       "Yes" = c("1", "2", "3", "4", "5", "6", "7", "8"),
                                       "No" = c("0"),
                                       "Unknown" = c("9"))) %>%
        mutate(INSURANCE_F = fct_recode(INSURANCE_STATUS,
                                         "None" = "0",
                                         "Private" = "1",
                                         "Medicaid" = "2",
                                         "Medicare" = "3",
                                         "Other Government" = "4",
                                         "Unknown" = "9")) %>%
        mutate(INSURANCE_F = fct_relevel(INSURANCE_F,
                                         "Private")) %>%
        mutate(INCOME_F = fct_recode(MED_INC_QUAR_12,
                                      "Less than $38,000" = "1",
                                      "$38,000 - $47,999" = "2",
                                      "$48,000 - $62,999" = "3",
                                      "$63,000 +" = "4")) %>%
        mutate(EDUCATION_F = fct_recode(NO_HSD_QUAR_12,
                                        "21% or more" = "1",
                                        "13 - 20.9%" = "2",
                                        "7 - 12.9%" = "3",
                                        "Less than 7%" = "4")) %>%
        mutate(U_R_F = fct_collapse(UR_CD_13,
                                    "Metro" = c("1", "2", "3"),
                                    "Urban" = c("4", "5", "6", "7"),
                                    "Rural" = c("8", "9"))) %>%
        mutate(CLASS_OF_CASE_F = fct_collapse(CLASS_OF_CASE,
                                              All_Part_Prim = c("10", "11", "12", "13",
                                                                "14", "20", "21", "22"),
                                              Other_Facility = c("00"))) %>%
        mutate(GRADE_F = fct_recode(GRADE,
                                  "Gr I: Well Diff" = "1",
                                  "Gr II: Mod Diff" = "2",
                                  "Gr III: Poor Diff" = "3",
                                  "Gr IV: Undiff/Anaplastic" = "4",
                                  "NA/Unkown" = "9")) %>%
        mutate(HISTOLOGY_F = fct_infreq(HISTOLOGY)) %>%
        mutate(HISTOLOGY_F = factor(HISTOLOGY_F)) %>%
        mutate(HISTOLOGY_F_LIM = fct_lump(HISTOLOGY_F, prop = 0.05)) %>%
        mutate(TNM_CLIN_T = fct_recode(TNM_CLIN_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_T = fct_relevel(TNM_CLIN_T,
                                        "1")) %>%
        mutate(TNM_CLIN_N = fct_recode(TNM_CLIN_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_M = fct_recode(TNM_CLIN_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_recode(TNM_PATH_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_relevel(TNM_PATH_T,
                                        "1")) %>%
        mutate(TNM_PATH_N = fct_recode(TNM_PATH_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_M = fct_recode(TNM_PATH_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_STAGE_GROUP = fct_recode(TNM_CLIN_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_STAGE_GROUP = fct_recode(TNM_PATH_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(MARGINS = fct_recode(RX_SUMM_SURGICAL_MARGINS,
                                    "No Residual" = "0",
                                    "Residual, NOS" = "1",
                                    "Microscopic Resid" = "2",
                                    "Macroscopic Resid" = "3",
                                    "Not evaluable" = "7",
                                    "No surg" = "8",
                                    "Unknown" = "9")) %>%
        mutate(MARGINS_YN = fct_collapse(RX_SUMM_SURGICAL_MARGINS,
                                         "Yes" = c("1", "2", "3"),
                                         "No" = c("0"),
                                         "No surg/Unk/NA" = c("7", "8", "9"))) %>%
        mutate(READM_HOSP_30_DAYS_F = fct_recode(READM_HOSP_30_DAYS,
                                                 "No_Surg_or_No_Readmit" = "0",
                                                 "Unplan_Readmit_Same" = "1",
                                                 "Plan_Readmit_Same" = "2",
                                                 "PlanUnplan_Same" = "3",
                                                 "Unknown" = "4")) %>%
        mutate(RX_SUMM_RADIATION_F = fct_recode(RX_SUMM_RADIATION,
                                                "None" = "0",
                                                "Beam Radiation" = "1",
                                                "Radioactive Implants" = "2",
                                                "Radioisotopes" = "3",
                                                "Beam + Imp or Isotopes" = "4",
                                                "Radiation, NOS" = "5",
                                                "Unknown" = "9")) %>%
        mutate(PUF_30_DAY_MORT_CD_F = fct_recode(PUF_30_DAY_MORT_CD,
                                                 "Alive_30" = "0",
                                                 "Dead_30" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(PUF_90_DAY_MORT_CD_F = fct_recode(PUF_90_DAY_MORT_CD,
                                                 "Alive_90" = "0",
                                                 "Dead_90" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(LYMPH_VASCULAR_INVASION_F = fct_recode(LYMPH_VASCULAR_INVASION,
                                                      "Neg_LymphVasc_Inv" = "0",
                                                      "Pos_LumphVasc_Inv" = "1",
                                                      "N_A" = "8",
                                                      "Unknown" = "9")) %>%
        mutate(RX_HOSP_SURG_APPR_2010_F = fct_recode(RX_HOSP_SURG_APPR_2010,
                                                     "No_Surg" = "0",
                                                     "Robot_Assist" = "1",
                                                     "Robot_to_Open" = "2",
                                                     "Endo_Lap" = "3",
                                                     "Endo_Lap_to_Open" = "4",
                                                     "Open_Unknown" = "5",
                                                     "Unknown" = "9")) %>%
        mutate(All = "All") %>%
        mutate(All = factor(All)) %>%
        mutate(REASON_FOR_NO_SURGERY_F = fct_recode(REASON_FOR_NO_SURGERY,
                                                    "Surg performed" = "0",
                                                    "Surg not recommended" = "1",
                                                    "No surg due to pt factors" = "2",
                                                    "No surg, pt died" = "5",
                                                    "Surg rec, not done" = "6",
                                                    "Surg rec, pt refused" = "7",
                                                    "Surg rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(SURGERY_YN = ifelse(REASON_FOR_NO_SURGERY == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_SURGERY == "9",
                                          "Ukn",
                                          "No"))) %>%
        mutate(SURG_TF = case_when(SURGERY_YN == "Yes" ~ TRUE,
                             SURGERY_YN == "No" ~ FALSE,
                             TRUE ~ NA))  %>%
        mutate(REASON_FOR_NO_RADIATION_F = fct_recode(REASON_FOR_NO_RADIATION,
                                                    "Rad performed" = "0",
                                                    "Rad not recommended" = "1",
                                                    "No Rad due to pt factors" = "2",
                                                    "No Rad, pt died" = "5",
                                                    "Rad rec, not done" = "6",
                                                    "Rad rec, pt refused" = "7",
                                                    "Rad rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(RADIATION_YN = ifelse(REASON_FOR_NO_RADIATION == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_RADIATION == "9",
                                          NA,
                                          "No"))) %>%
        mutate(SURGRAD_SEQ_F = fct_recode(RX_SUMM_SURGRAD_SEQ,
                                                   "None or Surg or Rad" = "0",
                                                   "Rad before Surg" = "2",
                                                   "Surg before Rad" = "3",
                                                   "Rad before and after Surg" = "4",
                                                   "Intraop Rad" = "5",
                                                   "Intraop Rad plus other" = "6",
                                                   "Unknown" = "9")) %>%
        mutate(SURG_RAD_SEQ = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                     "Surg Alone",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                            "Rad Alone",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0",
                                                   "No Treatment",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2",
                                                          "Rad then Surg",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3",
                                                                 "Surg then Rad",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4",
                                                                        "Rad before and after Surg",
                                                                        "Other"))))))) %>%
        mutate(SURG_RAD_SEQ = fct_relevel(SURG_RAD_SEQ,
                                          "Surg Alone",
                                          "Surg then Rad",
                                          "Rad Alone")) %>%
        mutate(CHEMO_YN = fct_collapse(RX_SUMM_CHEMO,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01", "02", "03"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(SURG_RAD_SEQ_C = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                     "Surg, No rad, No Chemo",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                            "Rad, No Surg, No Chemo",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                                   "No Surg, No Rad, No Chemo",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "No",
                                                          "Rad then Surg, No Chemo",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "No",
                                                                 "Surg then Rad, No Chemo",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "No",
                                                                        "Rad before and after Surg, No Chemo",
                                ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                       "Surg, No rad, Yes Chemo",
                                       ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                              "Rad, No Surg, Yes Chemo",
                                              ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                                     "No Surg, No Rad, Yes Chemo",
                                                     ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "Yes",
                                                            "Rad then Surg, Yes Chemo",
                                                            ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "Yes",
                                                                   "Surg then Rad, Yes Chemo",
                                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "Yes",
                                                                          "Rad before and after Surg, Yes Chemo",
                                                                          "Other"))))))))))))) %>%
        mutate(SURG_RAD_SEQ_C = fct_infreq(SURG_RAD_SEQ_C)) %>%
        mutate(T_SIZE = as.numeric(TUMOR_SIZE)) %>%
        mutate(T_SIZE = ifelse(T_SIZE == 0,
                                "No Tumor",
                                ifelse(T_SIZE > 0 & T_SIZE < 10 | T_SIZE == 991,
                                       "< 1 cm",
                                       ifelse(T_SIZE >= 10 & T_SIZE < 20 | T_SIZE == 992,
                                              "1-2 cm",
                                              ifelse(T_SIZE >= 20 & T_SIZE < 30 | T_SIZE == 993,
                                                     "2-3 cm",
                                                     ifelse(T_SIZE >= 30 & T_SIZE < 40 | T_SIZE == 994,
                                                            "3-4 cm",
                                                            ifelse(T_SIZE >= 40 & T_SIZE < 50 | T_SIZE == 995,
                                                                   "4-5 cm",
                                                                   ifelse(T_SIZE >= 50 & T_SIZE < 60 | T_SIZE == 996,
                                                                          "5-6 cm",
                                                                          ifelse(T_SIZE >= 60 & T_SIZE <= 987 |
                                                                                         T_SIZE == 980 | T_SIZE == 989 |
                                                                                         T_SIZE == 997,
                                                                          ">6 cm",
                                                                          ifelse(T_SIZE == 988 | T_SIZE == 999,
                                                                                 "NA_unk",
                                                                                 "Microscopic focus")))))))))) %>%
        mutate(T_SIZE = factor(T_SIZE)) %>%
        mutate(T_SIZE = fct_relevel(T_SIZE,
                                     "No Tumor", "Microscopic focus", "< 1 cm", "1-2 cm", "2-3 cm", "3-4 cm",
                                       "4-5 cm", "5-6 cm", ">6 cm", "NA_unk")) %>%
        mutate(mets_at_dx = case_when(CS_METS_DX_LUNG == "1" ~ "Lung",
                                      CS_METS_DX_BONE == "1" ~ "Bone",
                                      CS_METS_DX_BRAIN == "1" ~ "Brain",
                                      CS_METS_DX_LIVER == "1" ~ "Liver",
                                      TRUE ~ "None/Other/Unk/NA")) %>%
        mutate(MEDICAID_EXPN_CODE = fct_recode(MEDICAID_EXPN_CODE,
                                               "Non-Expansion State" = "0",
                                               "Jan 2014 Expansion States" = "1",
                                               "Early Expansion States (2010-13)" = "2",
                                               "Late Expansion States (> Jan 2014)" = "3",
                                               "Suppressed for Ages 0 - 39" = "9"))  %>%
        mutate(EXPN_GROUP =  case_when(MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Post-Expansion",
                                       
                                       MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% 
                                          c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013") ~ "Pre-Expansion",
               
                                       MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2010", "2011", "2012", "2013", "2014", "2015") ~ "Post-Expansion",
                                       
                                        MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", "2009") ~ "Pre-Expansion",
                                       MEDICAID_EXPN_CODE %in% c("Non-Expansion State") ~ "Pre-Expansion",
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") ~ "Pre-Expansion",
                    
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") & 
                                        YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Exclude",
                                       
                                       MEDICAID_EXPN_CODE == "Suppressed for Ages 0 - 39" ~ "Exclude")) %>%
  
  mutate(pre_2014 = YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013")) %>%
  
  mutate(mets_at_dx_F = ifelse(mets_at_dx == "None/Other/Unk/NA", FALSE, TRUE)) %>% 
  
  mutate(Tx_YN = ifelse(SURG_RAD_SEQ == "No Treatment" & CHEMO_YN == "No", FALSE, 
                        ifelse(CHEMO_YN == "Ukn", NA, 
                               TRUE)))
fact_vars_2 <- c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "AGE_F", "SEX_F", "RACE_F",
                 "HISPANIC", "INSURANCE_F", "INCOME_F", "EDUCATION_F", "U_R_F",
                 "CDCC_TOTAL_BEST", "CLASS_OF_CASE_F", "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "HISTOLOGY",
                 "BEHAVIOR", "GRADE_F", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M", "TNM_PATH_STAGE_GROUP",
                 "MARGINS", "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "mets_at_dx")
dat <- dat %>%
        mutate_at(fact_vars_2, funs(factor(.)))

Extract Data of Interest

# Melanoma
site_code <- c(
  #lip  
  "C000", "C001", "C002", "C003", "C004", "C005","C006", "C008","C009",
                                  
                                 
#skin/melanoma
  "C440", "C441", "C442", "C443", "C444", "C445", "C446", "C447", "C448", "C449",
                                 
 #breast - nipple
  "C500",
                                 
#vagina/vulva
  "C510", "C511", "C512", "C518", "C519", "C529",
                                 
#penis
 "C600", "C601", "C602", "C608", "C609", "C639")
histo_code <- c("8720", "8741", "8746", "8721", "8722", "8723", "8730", "8740", "8742", "8743", "8744", "8745", "8761")
behavior_code <- c("3")
data <- dat %>%
        filter(BEHAVIOR %in% behavior_code) %>%
        filter(PRIMARY_SITE %in% site_code) %>%
        filter(HISTOLOGY %in% histo_code) %>%
        filter(is.na(PUF_VITAL_STATUS) == FALSE) %>%
        filter(SEQUENCE_NUMBER == "00") %>%
        filter(is.na(DX_LASTCONTACT_DEATH_MONTHS) == FALSE) %>%
        filter(INSURANCE_STATUS %in% c("0", "1", "2", "3", "4"))
   # filter(AGE >= 18) %>%
   #      filter(TNM_CLIN_M %in% c("c0")) %>%
   #      
   #      filter(CLASS_OF_CASE %in% c("10", "12", "14", "22")) %>%
        
no_Excludes <- as.data.frame(data %>% 
                               filter(EXPN_GROUP != "Exclude") 
                             %>% droplevels())
file_path <- c("/Users/beastatlife/Google Drive/Penn/Research/Barbieri/NCDB")
save(data,
      file = paste0(file_path, "/melanoma_data.Rda"))
#load("melanoma_data.Rda")

Data including skin tumors was obtained from the NCBD on October 7, 2019. Cases that were included in this analysis were those with:

  1. Site codes: C000, C001, C002, C003, C004, C005, C006, C008, C009, C440, C441, C442, C443, C444, C445, C446, C447, C448, C449, C500, C510, C511, C512, C518, C519, C529, C600, C601, C602, C608, C609, C639
  2. Histology codes: 9700, 9701, 9708, 9709, 9718, 9719, 9726, 9727
  3. Behavior codes: 3

Patients were excluded if they didn’t have values for either follow up or vital status.

Patients were excluded if they had surgery to a distant site using RX_SUMM_SURG_OTH_REGDIS. This was done to avoid confounding of different surgical procedures. We are only interested in surgery at the primary site. These distant site surgeries were being counted in the surgery/radiation sequence and thus to simplify analysis they were removed.

data %>%
        CreateTableOne(data = .,
                     vars = c("RX_SUMM_SURG_OTH_REGDIS"),
                     includeNA = TRUE) %>%
        print(.,
              showAllLevels = TRUE)
                             
                              level Overall       
  n                                 281600        
  RX_SUMM_SURG_OTH_REGDIS (%) 0     269089 (95.6) 
                              1       4186 ( 1.5) 
                              2       3025 ( 1.1) 
                              3        702 ( 0.2) 
                              4       3590 ( 1.3) 
                              5        637 ( 0.2) 
                              9        371 ( 0.1) 
data <- data %>%
        filter(RX_SUMM_SURG_OTH_REGDIS == "0") 

Race was grouped as white, black, asian, other/unknown Stage was grouped into 0, I, II, III, IV, NA_Unknown, stage 0 was removed Whether surgery was performed was based on the REASON_FOR_NO_SURGERY variable. The SURGERY_YN variable was classified as ‘Yes’, ‘No’, or ‘Unknown’.

Whether radiation was performed was based on the REASON_FOR_NO_RADIATION variable. The RADIATION_YN variable was classified as ‘Yes’, ‘No’, or ‘Unknown’.

Table of variables for all cases:

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "HISTOLOGY_F_LIM", "HISTOLOGY_F", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE", "EXPN_GROUP", "SITE_TEXT"))
The data frame does not have: SITE_TEXT.1  Dropped

level Overall
n 269089
FACILITY_TYPE_F (%) Community Cancer Program 15035 ( 5.6)
Comprehensive Comm Ca Program 82869 ( 30.8)
Academic/Research Program 105617 ( 39.2)
Integrated Network Ca Program 27626 ( 10.3)
NA 37942 ( 14.1)
FACILITY_LOCATION_F (%) New England 14633 ( 5.4)
Middle Atlantic 37321 ( 13.9)
South Atlantic 52385 ( 19.5)
East North Central 39474 ( 14.7)
East South Central 15651 ( 5.8)
West North Central 19556 ( 7.3)
West South Central 12471 ( 4.6)
Mountain 11965 ( 4.4)
Pacific 27691 ( 10.3)
NA 37942 ( 14.1)
FACILITY_GEOGRAPHY (%) Northeast 51954 ( 19.3)
South 64856 ( 24.1)
Midwest 74681 ( 27.8)
West 39656 ( 14.7)
NA 37942 ( 14.1)
AGE (mean (sd)) 58.30 (16.60)
AGE_F (%) (0,54] 107677 ( 40.0)
(54,64] 60646 ( 22.5)
(64,74] 51176 ( 19.0)
(74,100] 49556 ( 18.4)
NA 34 ( 0.0)
AGE_40 (%) (0,40] 41135 ( 15.3)
(40,100] 227920 ( 84.7)
NA 34 ( 0.0)
SEX_F (%) Male 147088 ( 54.7)
Female 122001 ( 45.3)
RACE_F (%) White 261259 ( 97.1)
Black 1814 ( 0.7)
Other/Unk 5100 ( 1.9)
Asian 916 ( 0.3)
HISPANIC (%) No 249614 ( 92.8)
Yes 4270 ( 1.6)
Unknown 15205 ( 5.7)
INSURANCE_F (%) Private 158775 ( 59.0)
None 7722 ( 2.9)
Medicaid 7837 ( 2.9)
Medicare 91803 ( 34.1)
Other Government 2952 ( 1.1)
Unknown 0 ( 0.0)
INCOME_F (%) Less than $38,000 29575 ( 11.0)
$38,000 - $47,999 55271 ( 20.5)
$48,000 - $62,999 73392 ( 27.3)
$63,000 + 109534 ( 40.7)
NA 1317 ( 0.5)
EDUCATION_F (%) 21% or more 27806 ( 10.3)
13 - 20.9% 58222 ( 21.6)
7 - 12.9% 93536 ( 34.8)
Less than 7% 88374 ( 32.8)
NA 1151 ( 0.4)
U_R_F (%) Metro 220420 ( 81.9)
Urban 35956 ( 13.4)
Rural 4564 ( 1.7)
NA 8149 ( 3.0)
CROWFLY (mean (sd)) 32.55 (105.00)
CDCC_TOTAL_BEST (%) 0 234583 ( 87.2)
1 27581 ( 10.2)
2 5123 ( 1.9)
3 1802 ( 0.7)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 6 ( 0.0)
C00.1 External Lip: Lower NOS 8 ( 0.0)
C00.2 External Lip: NOS 1 ( 0.0)
C00.3 Lip: Upper Mucosa 3 ( 0.0)
C00.4 Lip: Lower Mucosa 6 ( 0.0)
C00.5 Lip: Mucosa NOS 2 ( 0.0)
C00.6 Lip: Commissure 1 ( 0.0)
C00.8 Lip: Overlapping 0 ( 0.0)
C00.9 Lip, NOS 1 ( 0.0)
C44.0 Skin of lip, NOS 451 ( 0.2)
C44.1 Eyelid 768 ( 0.3)
C44.2 External ear 7522 ( 2.8)
C44.3 Skin of ear and unspecified parts of face 22996 ( 8.5)
C44.4 Skin of scalp and neck 20868 ( 7.8)
C44.5 Skin of trunk 84369 ( 31.4)
C44.6 Skin of upper limb and shoulder 65905 ( 24.5)
C44.7 Skin of lower limb and hip 52572 ( 19.5)
C44.8 Overlapping lesion of skin 288 ( 0.1)
C44.9 Skin, NOS 11444 ( 4.3)
C50.0 Nipple 0 ( 0.0)
C51.0 Labium majus 127 ( 0.0)
C51.1 Labium minus 96 ( 0.0)
C51.2 Clitoris 46 ( 0.0)
C51.8 Overlapping lesion of vulva 46 ( 0.0)
C51.9 Vulva, NOS 1121 ( 0.4)
C52.9 Vagina, NOS 388 ( 0.1)
C60.0 Prepuce 8 ( 0.0)
C60.1 Glans penis 13 ( 0.0)
C60.2 Body of penis 2 ( 0.0)
C60.8 Overlapping lesion of penis 1 ( 0.0)
C60.9 Penis 30 ( 0.0)
HISTOLOGY_F_LIM (%) 8500 0 ( 0.0)
8720 136954 ( 50.9)
8520 0 ( 0.0)
Other 132135 ( 49.1)
HISTOLOGY_F (%) 8500 0 ( 0.0)
8720 136954 ( 50.9)
8520 0 ( 0.0)
8523 0 ( 0.0)
9680 0 ( 0.0)
8743 84190 ( 31.3)
8522 0 ( 0.0)
8070 0 ( 0.0)
8742 12140 ( 4.5)
8201 0 ( 0.0)
8501 0 ( 0.0)
8721 25612 ( 9.5)
8480 0 ( 0.0)
9663 0 ( 0.0)
8230 0 ( 0.0)
9690 0 ( 0.0)
9591 0 ( 0.0)
8010 0 ( 0.0)
9691 0 ( 0.0)
9650 0 ( 0.0)
9695 0 ( 0.0)
9673 0 ( 0.0)
8140 0 ( 0.0)
9699 0 ( 0.0)
8247 0 ( 0.0)
8507 0 ( 0.0)
9698 0 ( 0.0)
8211 0 ( 0.0)
9823 0 ( 0.0)
8071 0 ( 0.0)
8503 0 ( 0.0)
9670 0 ( 0.0)
9590 0 ( 0.0)
8050 0 ( 0.0)
8530 0 ( 0.0)
8575 0 ( 0.0)
9702 0 ( 0.0)
8524 0 ( 0.0)
9652 0 ( 0.0)
9687 0 ( 0.0)
8000 0 ( 0.0)
8832 0 ( 0.0)
8745 3657 ( 1.4)
8744 3666 ( 1.4)
8772 0 ( 0.0)
9714 0 ( 0.0)
8401 0 ( 0.0)
8510 0 ( 0.0)
9689 0 ( 0.0)
9659 0 ( 0.0)
9705 0 ( 0.0)
8542 0 ( 0.0)
9671 0 ( 0.0)
9020 0 ( 0.0)
8521 0 ( 0.0)
8410 0 ( 0.0)
8504 0 ( 0.0)
8543 0 ( 0.0)
8541 0 ( 0.0)
9651 0 ( 0.0)
9120 0 ( 0.0)
8200 0 ( 0.0)
8540 0 ( 0.0)
8090 0 ( 0.0)
9596 0 ( 0.0)
8730 926 ( 0.3)
8723 945 ( 0.4)
9837 0 ( 0.0)
8255 0 ( 0.0)
8072 0 ( 0.0)
8051 0 ( 0.0)
8830 0 ( 0.0)
8890 0 ( 0.0)
8771 0 ( 0.0)
9665 0 ( 0.0)
8770 0 ( 0.0)
8083 0 ( 0.0)
8761 619 ( 0.2)
8409 0 ( 0.0)
9729 0 ( 0.0)
9684 0 ( 0.0)
9653 0 ( 0.0)
8246 0 ( 0.0)
8081 0 ( 0.0)
9667 0 ( 0.0)
8390 0 ( 0.0)
8022 0 ( 0.0)
8560 0 ( 0.0)
8407 0 ( 0.0)
8413 0 ( 0.0)
8980 0 ( 0.0)
8076 0 ( 0.0)
8260 0 ( 0.0)
9727 0 ( 0.0)
8490 0 ( 0.0)
8097 0 ( 0.0)
8740 214 ( 0.1)
8032 0 ( 0.0)
8800 0 ( 0.0)
8481 0 ( 0.0)
8402 0 ( 0.0)
9735 0 ( 0.0)
9679 0 ( 0.0)
9811 0 ( 0.0)
8802 0 ( 0.0)
8041 0 ( 0.0)
9675 0 ( 0.0)
8310 0 ( 0.0)
8513 0 ( 0.0)
8052 0 ( 0.0)
8033 0 ( 0.0)
8400 0 ( 0.0)
9716 0 ( 0.0)
8343 0 ( 0.0)
8801 0 ( 0.0)
9827 0 ( 0.0)
8574 0 ( 0.0)
8323 0 ( 0.0)
8408 0 ( 0.0)
8380 0 ( 0.0)
8074 0 ( 0.0)
9719 0 ( 0.0)
8046 0 ( 0.0)
8502 0 ( 0.0)
8430 0 ( 0.0)
9664 0 ( 0.0)
8094 0 ( 0.0)
9728 0 ( 0.0)
8572 0 ( 0.0)
8746 95 ( 0.0)
8982 0 ( 0.0)
8833 0 ( 0.0)
8570 0 ( 0.0)
8573 0 ( 0.0)
8123 0 ( 0.0)
8403 0 ( 0.0)
9678 0 ( 0.0)
8525 0 ( 0.0)
8722 59 ( 0.0)
8020 0 ( 0.0)
9738 0 ( 0.0)
8804 0 ( 0.0)
8021 0 ( 0.0)
8141 0 ( 0.0)
8550 0 ( 0.0)
8811 0 ( 0.0)
8315 0 ( 0.0)
8810 0 ( 0.0)
8075 0 ( 0.0)
8940 0 ( 0.0)
8012 0 ( 0.0)
8805 0 ( 0.0)
8143 0 ( 0.0)
8780 0 ( 0.0)
9717 0 ( 0.0)
8910 0 ( 0.0)
9180 0 ( 0.0)
8935 0 ( 0.0)
8147 0 ( 0.0)
8013 0 ( 0.0)
8460 0 ( 0.0)
8091 0 ( 0.0)
8120 0 ( 0.0)
8092 0 ( 0.0)
8240 0 ( 0.0)
8512 0 ( 0.0)
8854 0 ( 0.0)
9655 0 ( 0.0)
8073 0 ( 0.0)
8850 0 ( 0.0)
8571 0 ( 0.0)
9709 0 ( 0.0)
8891 0 ( 0.0)
9580 0 ( 0.0)
8035 0 ( 0.0)
8453 0 ( 0.0)
8950 0 ( 0.0)
8562 0 ( 0.0)
8441 0 ( 0.0)
9737 0 ( 0.0)
8852 0 ( 0.0)
9708 0 ( 0.0)
8851 0 ( 0.0)
9540 0 ( 0.0)
8082 0 ( 0.0)
8420 0 ( 0.0)
8249 0 ( 0.0)
8741 12 ( 0.0)
8004 0 ( 0.0)
9724 0 ( 0.0)
8077 0 ( 0.0)
9718 0 ( 0.0)
8920 0 ( 0.0)
8983 0 ( 0.0)
9260 0 ( 0.0)
9700 0 ( 0.0)
8001 0 ( 0.0)
8508 0 ( 0.0)
9133 0 ( 0.0)
8080 0 ( 0.0)
8900 0 ( 0.0)
9044 0 ( 0.0)
8098 0 ( 0.0)
8084 0 ( 0.0)
8341 0 ( 0.0)
9071 0 ( 0.0)
8005 0 ( 0.0)
8144 0 ( 0.0)
8210 0 ( 0.0)
8896 0 ( 0.0)
8933 0 ( 0.0)
8963 0 ( 0.0)
8154 0 ( 0.0)
9220 0 ( 0.0)
8711 0 ( 0.0)
8894 0 ( 0.0)
8045 0 ( 0.0)
9654 0 ( 0.0)
8320 0 ( 0.0)
8514 0 ( 0.0)
8815 0 ( 0.0)
9473 0 ( 0.0)
8440 0 ( 0.0)
8803 0 ( 0.0)
8840 0 ( 0.0)
9130 0 ( 0.0)
9150 0 ( 0.0)
9701 0 ( 0.0)
8031 0 ( 0.0)
8130 0 ( 0.0)
8314 0 ( 0.0)
8858 0 ( 0.0)
9812 0 ( 0.0)
8124 0 ( 0.0)
8263 0 ( 0.0)
8470 0 ( 0.0)
9040 0 ( 0.0)
9560 0 ( 0.0)
8243 0 ( 0.0)
8450 0 ( 0.0)
8030 0 ( 0.0)
8078 0 ( 0.0)
8190 0 ( 0.0)
8251 0 ( 0.0)
8406 0 ( 0.0)
9100 0 ( 0.0)
8290 0 ( 0.0)
8461 0 ( 0.0)
8774 0 ( 0.0)
8901 0 ( 0.0)
8936 0 ( 0.0)
8093 0 ( 0.0)
8102 0 ( 0.0)
8148 0 ( 0.0)
8825 0 ( 0.0)
8931 0 ( 0.0)
8951 0 ( 0.0)
9364 0 ( 0.0)
9597 0 ( 0.0)
9662 0 ( 0.0)
8261 0 ( 0.0)
8384 0 ( 0.0)
8773 0 ( 0.0)
9041 0 ( 0.0)
9110 0 ( 0.0)
8011 0 ( 0.0)
8110 0 ( 0.0)
8344 0 ( 0.0)
8452 0 ( 0.0)
8806 0 ( 0.0)
9170 0 ( 0.0)
9231 0 ( 0.0)
9726 0 ( 0.0)
9814 0 ( 0.0)
9816 0 ( 0.0)
9817 0 ( 0.0)
8014 0 ( 0.0)
8095 0 ( 0.0)
8340 0 ( 0.0)
8482 0 ( 0.0)
8726 0 ( 0.0)
8835 0 ( 0.0)
8855 0 ( 0.0)
8902 0 ( 0.0)
8912 0 ( 0.0)
8930 0 ( 0.0)
9181 0 ( 0.0)
9813 0 ( 0.0)
9815 0 ( 0.0)
8015 0 ( 0.0)
8042 0 ( 0.0)
8053 0 ( 0.0)
8170 0 ( 0.0)
8252 0 ( 0.0)
8253 0 ( 0.0)
8262 0 ( 0.0)
8620 0 ( 0.0)
8710 0 ( 0.0)
8760 0 ( 0.0)
8836 0 ( 0.0)
8895 0 ( 0.0)
9043 0 ( 0.0)
9182 0 ( 0.0)
9240 0 ( 0.0)
9561 0 ( 0.0)
9581 0 ( 0.0)
9661 0 ( 0.0)
9818 0 ( 0.0)
8003 0 ( 0.0)
8100 0 ( 0.0)
8121 0 ( 0.0)
8131 0 ( 0.0)
8145 0 ( 0.0)
8150 0 ( 0.0)
8231 0 ( 0.0)
8245 0 ( 0.0)
8270 0 ( 0.0)
8312 0 ( 0.0)
8313 0 ( 0.0)
8319 0 ( 0.0)
8332 0 ( 0.0)
8347 0 ( 0.0)
8350 0 ( 0.0)
8471 0 ( 0.0)
8576 0 ( 0.0)
8583 0 ( 0.0)
8700 0 ( 0.0)
8750 0 ( 0.0)
8823 0 ( 0.0)
8831 0 ( 0.0)
8853 0 ( 0.0)
8941 0 ( 0.0)
8990 0 ( 0.0)
9000 0 ( 0.0)
9061 0 ( 0.0)
9064 0 ( 0.0)
9080 0 ( 0.0)
9085 0 ( 0.0)
9102 0 ( 0.0)
9105 0 ( 0.0)
9183 0 ( 0.0)
9186 0 ( 0.0)
9251 0 ( 0.0)
9370 0 ( 0.0)
9371 0 ( 0.0)
9451 0 ( 0.0)
9530 0 ( 0.0)
BEHAVIOR (%) 2 0 ( 0.0)
3 269089 (100.0)
GRADE_F (%) Gr I: Well Diff 589 ( 0.2)
Gr II: Mod Diff 816 ( 0.3)
Gr III: Poor Diff 1497 ( 0.6)
Gr IV: Undiff/Anaplastic 521 ( 0.2)
5 0 ( 0.0)
6 0 ( 0.0)
7 0 ( 0.0)
8 0 ( 0.0)
NA/Unkown 265666 ( 98.7)
DX_STAGING_PROC_DAYS (mean (sd)) 3.22 (53.98)
TNM_CLIN_T (%) N_A 1574 ( 0.6)
c0 3237 ( 1.2)
c1 14071 ( 5.2)
c1A 39363 ( 14.6)
c1B 15597 ( 5.8)
c1C 0 ( 0.0)
c1MI 0 ( 0.0)
c2 4134 ( 1.5)
c2A 25371 ( 9.4)
c2B 6395 ( 2.4)
c2C 0 ( 0.0)
c3 2398 ( 0.9)
c3A 9810 ( 3.6)
c3B 7776 ( 2.9)
c4 1674 ( 0.6)
c4A 4516 ( 1.7)
c4B 8256 ( 3.1)
c4C 0 ( 0.0)
c4D 0 ( 0.0)
cX 115943 ( 43.1)
pA 0 ( 0.0)
pIS 1760 ( 0.7)
NA 7214 ( 2.7)
TNM_CLIN_N (%) N_A 1573 ( 0.6)
c0 180888 ( 67.2)
c1 4432 ( 1.6)
c1A 1030 ( 0.4)
c1B 1267 ( 0.5)
c2 905 ( 0.3)
c2A 342 ( 0.1)
c2B 658 ( 0.2)
c2C 630 ( 0.2)
c3 2102 ( 0.8)
c3A 0 ( 0.0)
c3B 0 ( 0.0)
c3C 0 ( 0.0)
cX 69105 ( 25.7)
NA 6157 ( 2.3)
TNM_CLIN_M (%) N_A 1573 ( 0.6)
c0 245302 ( 91.2)
c0I+ 0 ( 0.0)
c1 3446 ( 1.3)
c1A 1045 ( 0.4)
c1B 1379 ( 0.5)
c1C 4150 ( 1.5)
NA 12194 ( 4.5)
TNM_CLIN_STAGE_GROUP (%) 0 2401 ( 0.9)
1 8331 ( 3.1)
1A 68926 ( 25.6)
1B 47613 ( 17.7)
1C 0 ( 0.0)
2 1514 ( 0.6)
2A 16357 ( 6.1)
2B 11360 ( 4.2)
2C 5992 ( 2.2)
3 8770 ( 3.3)
3A 0 ( 0.0)
3B 0 ( 0.0)
3C 0 ( 0.0)
4 10283 ( 3.8)
4A 2 ( 0.0)
4B 0 ( 0.0)
4C 1 ( 0.0)
N_A 1583 ( 0.6)
99 85926 ( 31.9)
NA 30 ( 0.0)
TNM_PATH_T (%) N_A 1571 ( 0.6)
p0 3652 ( 1.4)
p1 8932 ( 3.3)
p1A 43424 ( 16.1)
p1B 17703 ( 6.6)
p1C 0 ( 0.0)
p1MI 0 ( 0.0)
p2 2844 ( 1.1)
p2A 30821 ( 11.5)
p2B 6903 ( 2.6)
p3 1857 ( 0.7)
p3A 12774 ( 4.7)
p3B 9644 ( 3.6)
p4 1215 ( 0.5)
p4A 6629 ( 2.5)
p4B 11783 ( 4.4)
p4C 0 ( 0.0)
p4D 0 ( 0.0)
pA 0 ( 0.0)
pIS 936 ( 0.3)
pX 97279 ( 36.2)
NA 11122 ( 4.1)
TNM_PATH_N (%) N_A 1569 ( 0.6)
p0 121211 ( 45.0)
p0I- 0 ( 0.0)
p0I+ 0 ( 0.0)
p0M- 0 ( 0.0)
p0M+ 0 ( 0.0)
p1 3607 ( 1.3)
p1A 8421 ( 3.1)
p1B 1951 ( 0.7)
p1C 0 ( 0.0)
p1MI 0 ( 0.0)
p2 1104 ( 0.4)
p2A 2858 ( 1.1)
p2B 1480 ( 0.6)
p2C 1242 ( 0.5)
p3 3849 ( 1.4)
p3A 0 ( 0.0)
p3B 0 ( 0.0)
p3C 0 ( 0.0)
pX 101133 ( 37.6)
NA 20664 ( 7.7)
TNM_PATH_M (%) N_A 1552 ( 0.6)
p1 1529 ( 0.6)
p1A 828 ( 0.3)
p1B 626 ( 0.2)
p1C 1687 ( 0.6)
pX 116236 ( 43.2)
NA 146631 ( 54.5)
TNM_PATH_STAGE_GROUP (%) 0 1898 ( 0.7)
1 6027 ( 2.2)
1A 63103 ( 23.5)
1B 53543 ( 19.9)
1C 0 ( 0.0)
2 1254 ( 0.5)
2A 17789 ( 6.6)
2B 12097 ( 4.5)
2C 6201 ( 2.3)
3 5432 ( 2.0)
3A 8067 ( 3.0)
3B 7374 ( 2.7)
3C 5149 ( 1.9)
4 5747 ( 2.1)
4A 7 ( 0.0)
4B 0 ( 0.0)
4C 1 ( 0.0)
N_A 1576 ( 0.6)
99 65711 ( 24.4)
NA 8113 ( 3.0)
DX_RX_STARTED_DAYS (mean (sd)) 10.76 (27.08)
DX_SURG_STARTED_DAYS (mean (sd)) 10.12 (25.88)
DX_DEFSURG_STARTED_DAYS (mean (sd)) 31.11 (34.24)
MARGINS (%) No Residual 244357 ( 90.8)
Residual, NOS 3777 ( 1.4)
Microscopic Resid 3812 ( 1.4)
Macroscopic Resid 296 ( 0.1)
Not evaluable 746 ( 0.3)
No surg 12757 ( 4.7)
Unknown 3344 ( 1.2)
MARGINS_YN (%) No 244357 ( 90.8)
Yes 7885 ( 2.9)
No surg/Unk/NA 16847 ( 6.3)
SURG_DISCHARGE_DAYS (mean (sd)) 1.75 (9.08)
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 257592 ( 95.7)
Unplan_Readmit_Same 2551 ( 0.9)
Plan_Readmit_Same 3999 ( 1.5)
PlanUnplan_Same 395 ( 0.1)
9 4552 ( 1.7)
RX_SUMM_RADIATION_F (%) None 258472 ( 96.1)
Beam Radiation 8638 ( 3.2)
Radioactive Implants 68 ( 0.0)
Radioisotopes 5 ( 0.0)
Beam + Imp or Isotopes 46 ( 0.0)
Radiation, NOS 85 ( 0.0)
Unknown 1775 ( 0.7)
PUF_30_DAY_MORT_CD_F (%) Alive_30 248786 ( 92.5)
Dead_30 551 ( 0.2)
Unknown 6802 ( 2.5)
NA 12950 ( 4.8)
PUF_90_DAY_MORT_CD_F (%) Alive_90 243303 ( 90.4)
Dead_90 1737 ( 0.6)
Unknown 11099 ( 4.1)
NA 12950 ( 4.8)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 55.61 (41.49)
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 106669 ( 39.6)
Pos_LumphVasc_Inv 5852 ( 2.2)
N_A 34 ( 0.0)
Unknown 36814 ( 13.7)
NA 119720 ( 44.5)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 14021 ( 5.2)
Robot_Assist 117 ( 0.0)
Robot_to_Open 28 ( 0.0)
Endo_Lap 356 ( 0.1)
Endo_Lap_to_Open 131 ( 0.0)
Open_Unknown 134691 ( 50.1)
Unknown 25 ( 0.0)
NA 119720 ( 44.5)
SURG_RAD_SEQ (%) Surg Alone 249888 ( 92.9)
Surg then Rad 4529 ( 1.7)
Rad Alone 4187 ( 1.6)
No Treatment 8084 ( 3.0)
Other 2299 ( 0.9)
Rad before and after Surg 15 ( 0.0)
Rad then Surg 87 ( 0.0)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 240119 ( 89.2)
Surg then Rad, No Chemo 3653 ( 1.4)
Surg then Rad, Yes Chemo 736 ( 0.3)
Surg, No rad, Yes Chemo 3131 ( 1.2)
No Surg, No Rad, Yes Chemo 1704 ( 0.6)
No Surg, No Rad, No Chemo 6106 ( 2.3)
Other 9453 ( 3.5)
Rad, No Surg, Yes Chemo 1543 ( 0.6)
Rad, No Surg, No Chemo 2542 ( 0.9)
Rad then Surg, Yes Chemo 31 ( 0.0)
Rad then Surg, No Chemo 56 ( 0.0)
Rad before and after Surg, Yes Chemo 0 ( 0.0)
Rad before and after Surg, No Chemo 15 ( 0.0)
SURGERY_YN (%) No 12368 ( 4.6)
Ukn 510 ( 0.2)
Yes 256211 ( 95.2)
RADIATION_YN (%) No 258334 ( 96.0)
Yes 8842 ( 3.3)
NA 1913 ( 0.7)
CHEMO_YN (%) No 253329 ( 94.1)
Yes 7241 ( 2.7)
Ukn 8519 ( 3.2)
mets_at_dx (%) Bone 552 ( 0.2)
Brain 681 ( 0.3)
Liver 412 ( 0.2)
Lung 2504 ( 0.9)
None/Other/Unk/NA 264940 ( 98.5)
MEDICAID_EXPN_CODE (%) Non-Expansion State 83879 ( 31.2)
Jan 2014 Expansion States 71602 ( 26.6)
Early Expansion States (2010-13) 43372 ( 16.1)
Late Expansion States (> Jan 2014) 32294 ( 12.0)
Suppressed for Ages 0 - 39 37942 ( 14.1)
EXPN_GROUP (%) Exclude 37942 ( 14.1)
Post-Expansion 39323 ( 14.6)
Pre-Expansion 191824 ( 71.3)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 6 ( 0.0)
C00.1 External Lip: Lower NOS 8 ( 0.0)
C00.2 External Lip: NOS 1 ( 0.0)
C00.3 Lip: Upper Mucosa 3 ( 0.0)
C00.4 Lip: Lower Mucosa 6 ( 0.0)
C00.5 Lip: Mucosa NOS 2 ( 0.0)
C00.6 Lip: Commissure 1 ( 0.0)
C00.8 Lip: Overlapping 0 ( 0.0)
C00.9 Lip, NOS 1 ( 0.0)
C44.0 Skin of lip, NOS 451 ( 0.2)
C44.1 Eyelid 768 ( 0.3)
C44.2 External ear 7522 ( 2.8)
C44.3 Skin of ear and unspecified parts of face 22996 ( 8.5)
C44.4 Skin of scalp and neck 20868 ( 7.8)
C44.5 Skin of trunk 84369 ( 31.4)
C44.6 Skin of upper limb and shoulder 65905 ( 24.5)
C44.7 Skin of lower limb and hip 52572 ( 19.5)
C44.8 Overlapping lesion of skin 288 ( 0.1)
C44.9 Skin, NOS 11444 ( 4.3)
C50.0 Nipple 0 ( 0.0)
C51.0 Labium majus 127 ( 0.0)
C51.1 Labium minus 96 ( 0.0)
C51.2 Clitoris 46 ( 0.0)
C51.8 Overlapping lesion of vulva 46 ( 0.0)
C51.9 Vulva, NOS 1121 ( 0.4)
C52.9 Vagina, NOS 388 ( 0.1)
C60.0 Prepuce 8 ( 0.0)
C60.1 Glans penis 13 ( 0.0)
C60.2 Body of penis 2 ( 0.0)
C60.8 Overlapping lesion of penis 1 ( 0.0)
C60.9 Penis 30 ( 0.0)

p_table(no_Excludes,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "HISTOLOGY_F_LIM", "HISTOLOGY_F", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE","SITE_TEXT"), 
        strata = "EXPN_GROUP")
The data frame does not have: SITE_TEXT.1  Dropped

level Post-Expansion Pre-Expansion p test
n 40765 201262
FACILITY_TYPE_F (%) Community Cancer Program 2404 ( 5.9) 13221 ( 6.6) <0.001
Comprehensive Comm Ca Program 13293 ( 32.6) 73088 ( 36.3)
Academic/Research Program 21865 ( 53.6) 88862 ( 44.2)
Integrated Network Ca Program 3203 ( 7.9) 26091 ( 13.0)
FACILITY_LOCATION_F (%) New England 4060 ( 10.0) 11185 ( 5.6) <0.001
Middle Atlantic 8697 ( 21.3) 30179 ( 15.0)
South Atlantic 1736 ( 4.3) 53330 ( 26.5)
East North Central 4276 ( 10.5) 36885 ( 18.3)
East South Central 951 ( 2.3) 15730 ( 7.8)
West North Central 5041 ( 12.4) 15398 ( 7.7)
West South Central 97 ( 0.2) 13018 ( 6.5)
Mountain 1735 ( 4.3) 10876 ( 5.4)
Pacific 14172 ( 34.8) 14661 ( 7.3)
FACILITY_GEOGRAPHY (%) Northeast 12757 ( 31.3) 41364 ( 20.6) <0.001
South 1833 ( 4.5) 66348 ( 33.0)
Midwest 10268 ( 25.2) 68013 ( 33.8)
West 15907 ( 39.0) 25537 ( 12.7)
AGE (mean (sd)) 63.64 (12.73) 62.63 (12.98) <0.001
AGE_F (%) (0,54] 10962 ( 26.9) 62153 ( 30.9) <0.001
(54,64] 11191 ( 27.5) 52555 ( 26.1)
(64,74] 9704 ( 23.8) 43888 ( 21.8)
(74,100] 8908 ( 21.9) 42666 ( 21.2)
AGE_40 (%) (0,40] 452 ( 1.1) 2924 ( 1.5) <0.001
(40,100] 40313 ( 98.9) 198338 ( 98.5)
SEX_F (%) Male 23459 ( 57.5) 116717 ( 58.0) 0.098
Female 17306 ( 42.5) 84545 ( 42.0)
RACE_F (%) White 39584 ( 97.1) 195620 ( 97.2) <0.001
Black 209 ( 0.5) 1486 ( 0.7)
Other/Unk 738 ( 1.8) 3637 ( 1.8)
Asian 234 ( 0.6) 519 ( 0.3)
HISPANIC (%) No 38860 ( 95.3) 186065 ( 92.4) <0.001
Yes 886 ( 2.2) 2729 ( 1.4)
Unknown 1019 ( 2.5) 12468 ( 6.2)
INSURANCE_F (%) Private 21896 ( 53.7) 109947 ( 54.6) <0.001
None 625 ( 1.5) 5675 ( 2.8)
Medicaid 1602 ( 3.9) 4305 ( 2.1)
Medicare 16341 ( 40.1) 79051 ( 39.3)
Other Government 301 ( 0.7) 2284 ( 1.1)
INCOME_F (%) Less than $38,000 2282 ( 5.6) 24757 ( 12.3) <0.001
$38,000 - $47,999 5522 ( 13.5) 44589 ( 22.2)
$48,000 - $62,999 10053 ( 24.7) 55768 ( 27.7)
$63,000 + 22819 ( 56.0) 75047 ( 37.3)
NA 89 ( 0.2) 1101 ( 0.5)
EDUCATION_F (%) 21% or more 3424 ( 8.4) 22020 ( 10.9) <0.001
13 - 20.9% 6691 ( 16.4) 46145 ( 22.9)
7 - 12.9% 14306 ( 35.1) 69547 ( 34.6)
Less than 7% 16272 ( 39.9) 62578 ( 31.1)
NA 72 ( 0.2) 972 ( 0.5)
U_R_F (%) Metro 34980 ( 85.8) 162557 ( 80.8) <0.001
Urban 4233 ( 10.4) 28590 ( 14.2)
Rural 368 ( 0.9) 3890 ( 1.9)
NA 1184 ( 2.9) 6225 ( 3.1)
CROWFLY (mean (sd)) 29.62 (108.02) 33.37 (104.82) <0.001
CDCC_TOTAL_BEST (%) 0 34533 ( 84.7) 172265 ( 85.6) <0.001
1 4874 ( 12.0) 22974 ( 11.4)
2 944 ( 2.3) 4488 ( 2.2)
3 414 ( 1.0) 1535 ( 0.8)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 1 ( 0.0) 5 ( 0.0) 0.004
C00.1 External Lip: Lower NOS 1 ( 0.0) 7 ( 0.0)
C00.2 External Lip: NOS 1 ( 0.0) 0 ( 0.0)
C00.3 Lip: Upper Mucosa 0 ( 0.0) 4 ( 0.0)
C00.4 Lip: Lower Mucosa 1 ( 0.0) 5 ( 0.0)
C00.5 Lip: Mucosa NOS 1 ( 0.0) 1 ( 0.0)
C00.9 Lip, NOS 0 ( 0.0) 1 ( 0.0)
C44.0 Skin of lip, NOS 75 ( 0.2) 357 ( 0.2)
C44.1 Eyelid 120 ( 0.3) 615 ( 0.3)
C44.2 External ear 1129 ( 2.8) 5782 ( 2.9)
C44.3 Skin of ear and unspecified parts of face 3518 ( 8.6) 18335 ( 9.1)
C44.4 Skin of scalp and neck 3165 ( 7.8) 15842 ( 7.9)
C44.5 Skin of trunk 12136 ( 29.8) 59770 ( 29.7)
C44.6 Skin of upper limb and shoulder 10107 ( 24.8) 49284 ( 24.5)
C44.7 Skin of lower limb and hip 7696 ( 18.9) 36379 ( 18.1)
C44.8 Overlapping lesion of skin 50 ( 0.1) 230 ( 0.1)
C44.9 Skin, NOS 2474 ( 6.1) 13105 ( 6.5)
C51.0 Labium majus 14 ( 0.0) 98 ( 0.0)
C51.1 Labium minus 14 ( 0.0) 81 ( 0.0)
C51.2 Clitoris 2 ( 0.0) 45 ( 0.0)
C51.8 Overlapping lesion of vulva 8 ( 0.0) 36 ( 0.0)
C51.9 Vulva, NOS 177 ( 0.4) 888 ( 0.4)
C52.9 Vagina, NOS 67 ( 0.2) 347 ( 0.2)
C60.0 Prepuce 1 ( 0.0) 7 ( 0.0)
C60.1 Glans penis 2 ( 0.0) 11 ( 0.0)
C60.2 Body of penis 0 ( 0.0) 2 ( 0.0)
C60.8 Overlapping lesion of penis 0 ( 0.0) 1 ( 0.0)
C60.9 Penis 5 ( 0.0) 24 ( 0.0)
HISTOLOGY_F_LIM (%) 8720 22006 ( 54.0) 103751 ( 51.6) <0.001
Other 18759 ( 46.0) 97511 ( 48.4)
HISTOLOGY_F (%) 8720 22006 ( 54.0) 103751 ( 51.6) <0.001
8743 11481 ( 28.2) 59012 ( 29.3)
8742 1869 ( 4.6) 10229 ( 5.1)
8721 3845 ( 9.4) 20211 ( 10.0)
8745 604 ( 1.5) 2994 ( 1.5)
8744 567 ( 1.4) 2923 ( 1.5)
8730 185 ( 0.5) 772 ( 0.4)
8723 100 ( 0.2) 751 ( 0.4)
8761 59 ( 0.1) 349 ( 0.2)
8740 29 ( 0.1) 135 ( 0.1)
8746 11 ( 0.0) 81 ( 0.0)
8722 6 ( 0.0) 46 ( 0.0)
8741 3 ( 0.0) 8 ( 0.0)
BEHAVIOR (%) 3 40765 (100.0) 201262 (100.0) NA
GRADE_F (%) Gr I: Well Diff 61 ( 0.1) 457 ( 0.2) <0.001
Gr II: Mod Diff 103 ( 0.3) 605 ( 0.3)
Gr III: Poor Diff 188 ( 0.5) 1322 ( 0.7)
Gr IV: Undiff/Anaplastic 89 ( 0.2) 423 ( 0.2)
NA/Unkown 40324 ( 98.9) 198455 ( 98.6)
DX_STAGING_PROC_DAYS (mean (sd)) 2.15 (20.06) 3.85 (60.75) 0.001
TNM_CLIN_T (%) N_A 65 ( 0.2) 1465 ( 0.7) <0.001
c0 905 ( 2.2) 3598 ( 1.8)
c1 2165 ( 5.3) 9944 ( 4.9)
c1A 10863 ( 26.6) 23020 ( 11.4)
c1B 4022 ( 9.9) 9253 ( 4.6)
c2 644 ( 1.6) 3084 ( 1.5)
c2A 4403 ( 10.8) 17993 ( 8.9)
c2B 1226 ( 3.0) 4723 ( 2.3)
c3 369 ( 0.9) 1896 ( 0.9)
c3A 1758 ( 4.3) 7136 ( 3.5)
c3B 1506 ( 3.7) 5931 ( 2.9)
c4 279 ( 0.7) 1355 ( 0.7)
c4A 767 ( 1.9) 3522 ( 1.7)
c4B 1550 ( 3.8) 6536 ( 3.2)
cX 7184 ( 17.6) 96462 ( 47.9)
pIS 397 ( 1.0) 1285 ( 0.6)
NA 2662 ( 6.5) 4059 ( 2.0)
TNM_CLIN_N (%) N_A 64 ( 0.2) 1465 ( 0.7) <0.001
c0 32367 ( 79.4) 127138 ( 63.2)
c1 954 ( 2.3) 3525 ( 1.8)
c1A 99 ( 0.2) 855 ( 0.4)
c1B 148 ( 0.4) 1141 ( 0.6)
c2 173 ( 0.4) 732 ( 0.4)
c2A 40 ( 0.1) 288 ( 0.1)
c2B 90 ( 0.2) 590 ( 0.3)
c2C 131 ( 0.3) 568 ( 0.3)
c3 423 ( 1.0) 1908 ( 0.9)
cX 3959 ( 9.7) 59602 ( 29.6)
NA 2317 ( 5.7) 3450 ( 1.7)
TNM_CLIN_M (%) N_A 63 ( 0.2) 1466 ( 0.7) <0.001
c0 34085 ( 83.6) 182329 ( 90.6)
c1 499 ( 1.2) 3838 ( 1.9)
c1A 255 ( 0.6) 1179 ( 0.6)
c1B 336 ( 0.8) 1367 ( 0.7)
c1C 1217 ( 3.0) 4115 ( 2.0)
NA 4310 ( 10.6) 6968 ( 3.5)
TNM_CLIN_STAGE_GROUP (%) 0 521 ( 1.3) 1766 ( 0.9) <0.001
1 1278 ( 3.1) 5862 ( 2.9)
1A 11735 ( 28.8) 46419 ( 23.1)
1B 8403 ( 20.6) 32864 ( 16.3)
2 219 ( 0.5) 1155 ( 0.6)
2A 2925 ( 7.2) 12011 ( 6.0)
2B 2044 ( 5.0) 8733 ( 4.3)
2C 1139 ( 2.8) 4693 ( 2.3)
3 1501 ( 3.7) 7265 ( 3.6)
4 2400 ( 5.9) 10709 ( 5.3)
4A 1 ( 0.0) 2 ( 0.0)
4C 0 ( 0.0) 1 ( 0.0)
N_A 70 ( 0.2) 1468 ( 0.7)
99 8516 ( 20.9) 68296 ( 33.9)
NA 13 ( 0.0) 18 ( 0.0)
TNM_PATH_T (%) N_A 63 ( 0.2) 1464 ( 0.7) <0.001
p0 795 ( 2.0) 3002 ( 1.5)
p1 1205 ( 3.0) 6468 ( 3.2)
p1A 12275 ( 30.1) 25155 ( 12.5)
p1B 4672 ( 11.5) 10356 ( 5.1)
p2 314 ( 0.8) 2220 ( 1.1)
p2A 5621 ( 13.8) 21457 ( 10.7)
p2B 1335 ( 3.3) 5035 ( 2.5)
p3 258 ( 0.6) 1505 ( 0.7)
p3A 2352 ( 5.8) 9240 ( 4.6)
p3B 1875 ( 4.6) 7317 ( 3.6)
p4 195 ( 0.5) 1018 ( 0.5)
p4A 1298 ( 3.2) 5075 ( 2.5)
p4B 2424 ( 5.9) 9207 ( 4.6)
pIS 170 ( 0.4) 722 ( 0.4)
pX 2525 ( 6.2) 84386 ( 41.9)
NA 3388 ( 8.3) 7635 ( 3.8)
TNM_PATH_N (%) N_A 62 ( 0.2) 1463 ( 0.7) <0.001
p0 20428 ( 50.1) 85526 ( 42.5)
p1 551 ( 1.4) 2889 ( 1.4)
p1A 1592 ( 3.9) 5920 ( 2.9)
p1B 368 ( 0.9) 1576 ( 0.8)
p2 151 ( 0.4) 910 ( 0.5)
p2A 571 ( 1.4) 1933 ( 1.0)
p2B 281 ( 0.7) 1220 ( 0.6)
p2C 268 ( 0.7) 1047 ( 0.5)
p3 775 ( 1.9) 3296 ( 1.6)
pX 9689 ( 23.8) 82003 ( 40.7)
NA 6029 ( 14.8) 13479 ( 6.7)
TNM_PATH_M (%) N_A 58 ( 0.1) 1449 ( 0.7) <0.001
p1 178 ( 0.4) 2036 ( 1.0)
p1A 187 ( 0.5) 1030 ( 0.5)
p1B 163 ( 0.4) 746 ( 0.4)
p1C 548 ( 1.3) 2104 ( 1.0)
pX 0 ( 0.0) 101181 ( 50.3)
NA 39631 ( 97.2) 92716 ( 46.1)
TNM_PATH_STAGE_GROUP (%) 0 348 ( 0.9) 1351 ( 0.7) <0.001
1 812 ( 2.0) 4286 ( 2.1)
1A 9377 ( 23.0) 43512 ( 21.6)
1B 8275 ( 20.3) 37802 ( 18.8)
2 142 ( 0.3) 979 ( 0.5)
2A 2792 ( 6.8) 13409 ( 6.7)
2B 1994 ( 4.9) 9433 ( 4.7)
2C 1130 ( 2.8) 4920 ( 2.4)
3 540 ( 1.3) 4564 ( 2.3)
3A 1401 ( 3.4) 5654 ( 2.8)
3B 1255 ( 3.1) 5649 ( 2.8)
3C 998 ( 2.4) 4181 ( 2.1)
4 1312 ( 3.2) 6899 ( 3.4)
4A 1 ( 0.0) 7 ( 0.0)
4C 0 ( 0.0) 1 ( 0.0)
N_A 66 ( 0.2) 1465 ( 0.7)
99 8155 ( 20.0) 51615 ( 25.6)
NA 2167 ( 5.3) 5535 ( 2.8)
DX_RX_STARTED_DAYS (mean (sd)) 12.48 (26.15) 11.03 (28.24) <0.001
DX_SURG_STARTED_DAYS (mean (sd)) 11.85 (25.57) 10.43 (27.30) <0.001
DX_DEFSURG_STARTED_DAYS (mean (sd)) 34.03 (32.15) 30.73 (34.78) <0.001
MARGINS (%) No Residual 36028 ( 88.4) 177483 ( 88.2) <0.001
Residual, NOS 563 ( 1.4) 3121 ( 1.6)
Microscopic Resid 728 ( 1.8) 2965 ( 1.5)
Macroscopic Resid 45 ( 0.1) 252 ( 0.1)
Not evaluable 125 ( 0.3) 617 ( 0.3)
No surg 2846 ( 7.0) 14061 ( 7.0)
Unknown 430 ( 1.1) 2763 ( 1.4)
MARGINS_YN (%) No 36028 ( 88.4) 177483 ( 88.2) 0.050
Yes 1336 ( 3.3) 6338 ( 3.1)
No surg/Unk/NA 3401 ( 8.3) 17441 ( 8.7)
SURG_DISCHARGE_DAYS (mean (sd)) 0.78 (5.52) 2.02 (9.82) <0.001
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 39943 ( 98.0) 191472 ( 95.1) <0.001
Unplan_Readmit_Same 203 ( 0.5) 2220 ( 1.1)
Plan_Readmit_Same 390 ( 1.0) 3307 ( 1.6)
PlanUnplan_Same 42 ( 0.1) 322 ( 0.2)
9 187 ( 0.5) 3941 ( 2.0)
RX_SUMM_RADIATION_F (%) None 38980 ( 95.6) 190623 ( 94.7) <0.001
Beam Radiation 1672 ( 4.1) 8904 ( 4.4)
Radioactive Implants 11 ( 0.0) 62 ( 0.0)
Radioisotopes 1 ( 0.0) 5 ( 0.0)
Beam + Imp or Isotopes 9 ( 0.0) 38 ( 0.0)
Radiation, NOS 14 ( 0.0) 90 ( 0.0)
Unknown 78 ( 0.2) 1540 ( 0.8)
PUF_30_DAY_MORT_CD_F (%) Alive_30 36561 ( 89.7) 182141 ( 90.5) <0.001
Dead_30 80 ( 0.2) 479 ( 0.2)
Unknown 1241 ( 3.0) 4407 ( 2.2)
NA 2883 ( 7.1) 14235 ( 7.1)
PUF_90_DAY_MORT_CD_F (%) Alive_90 35463 ( 87.0) 178281 ( 88.6) <0.001
Dead_90 236 ( 0.6) 1574 ( 0.8)
Unknown 2183 ( 5.4) 7172 ( 3.6)
NA 2883 ( 7.1) 14235 ( 7.1)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 31.19 (21.58) 58.30 (42.48) <0.001
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 28218 ( 69.2) 67005 ( 33.3) <0.001
Pos_LumphVasc_Inv 1566 ( 3.8) 4091 ( 2.0)
N_A 9 ( 0.0) 24 ( 0.0)
Unknown 10972 ( 26.9) 24575 ( 12.2)
NA 0 ( 0.0) 105567 ( 52.5)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 4647 ( 11.4) 11360 ( 5.6) <0.001
Robot_Assist 39 ( 0.1) 73 ( 0.0)
Robot_to_Open 6 ( 0.0) 18 ( 0.0)
Endo_Lap 87 ( 0.2) 244 ( 0.1)
Endo_Lap_to_Open 19 ( 0.0) 110 ( 0.1)
Open_Unknown 35958 ( 88.2) 83874 ( 41.7)
Unknown 9 ( 0.0) 16 ( 0.0)
NA 0 ( 0.0) 105567 ( 52.5)
SURG_RAD_SEQ (%) Surg Alone 37112 ( 91.0) 181500 ( 90.2) <0.001
Surg then Rad 991 ( 2.4) 5632 ( 2.8)
Rad Alone 676 ( 1.7) 3221 ( 1.6)
No Treatment 1804 ( 4.4) 8602 ( 4.3)
Other 146 ( 0.4) 2090 ( 1.0)
Rad before and after Surg 7 ( 0.0) 24 ( 0.0)
Rad then Surg 29 ( 0.1) 193 ( 0.1)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 35898 ( 88.1) 173939 ( 86.4) <0.001
Surg then Rad, No Chemo 782 ( 1.9) 4272 ( 2.1)
Surg then Rad, Yes Chemo 182 ( 0.4) 1204 ( 0.6)
Surg, No rad, Yes Chemo 349 ( 0.9) 2528 ( 1.3)
No Surg, No Rad, Yes Chemo 314 ( 0.8) 1734 ( 0.9)
No Surg, No Rad, No Chemo 1432 ( 3.5) 6538 ( 3.2)
Other 1113 ( 2.7) 7693 ( 3.8)
Rad, No Surg, Yes Chemo 208 ( 0.5) 1186 ( 0.6)
Rad, No Surg, No Chemo 451 ( 1.1) 1955 ( 1.0)
Rad then Surg, Yes Chemo 11 ( 0.0) 80 ( 0.0)
Rad then Surg, No Chemo 18 ( 0.0) 109 ( 0.1)
Rad before and after Surg, Yes Chemo 1 ( 0.0) 7 ( 0.0)
Rad before and after Surg, No Chemo 6 ( 0.0) 17 ( 0.0)
T_SIZE (%) Microscopic focus 0 ( 0.0) 5 ( 0.0) 0.542
< 1 cm 22 ( 0.1) 146 ( 0.1)
1-2 cm 53 ( 0.1) 260 ( 0.1)
2-3 cm 54 ( 0.1) 219 ( 0.1)
3-4 cm 21 ( 0.1) 130 ( 0.1)
4-5 cm 18 ( 0.0) 101 ( 0.1)
5-6 cm 13 ( 0.0) 54 ( 0.0)
>6 cm 24 ( 0.1) 146 ( 0.1)
NA_unk 90 ( 0.2) 502 ( 0.2)
NA 40470 ( 99.3) 199699 ( 99.2)
SURGERY_YN (%) No 2821 ( 6.9) 13640 ( 6.8) <0.001
Ukn 55 ( 0.1) 534 ( 0.3)
Yes 37889 ( 92.9) 187088 ( 93.0)
RADIATION_YN (%) No 38953 ( 95.6) 190471 ( 94.6) <0.001
Yes 1707 ( 4.2) 9099 ( 4.5)
NA 105 ( 0.3) 1692 ( 0.8)
CHEMO_YN (%) No 38664 ( 94.8) 187629 ( 93.2) <0.001
Yes 1074 ( 2.6) 6835 ( 3.4)
Ukn 1027 ( 2.5) 6798 ( 3.4)
mets_at_dx (%) Bone 175 ( 0.4) 453 ( 0.2) <0.001
Brain 320 ( 0.8) 849 ( 0.4)
Liver 111 ( 0.3) 318 ( 0.2)
Lung 912 ( 2.2) 2187 ( 1.1)
None/Other/Unk/NA 39247 ( 96.3) 197455 ( 98.1)
MEDICAID_EXPN_CODE (%) Non-Expansion State 0 ( 0.0) 88443 ( 43.9) <0.001
Jan 2014 Expansion States 15288 ( 37.5) 59573 ( 29.6)
Early Expansion States (2010-13) 25477 ( 62.5) 19494 ( 9.7)
Late Expansion States (> Jan 2014) 0 ( 0.0) 33752 ( 16.8)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 1 ( 0.0) 5 ( 0.0) 0.004
C00.1 External Lip: Lower NOS 1 ( 0.0) 7 ( 0.0)
C00.2 External Lip: NOS 1 ( 0.0) 0 ( 0.0)
C00.3 Lip: Upper Mucosa 0 ( 0.0) 4 ( 0.0)
C00.4 Lip: Lower Mucosa 1 ( 0.0) 5 ( 0.0)
C00.5 Lip: Mucosa NOS 1 ( 0.0) 1 ( 0.0)
C00.9 Lip, NOS 0 ( 0.0) 1 ( 0.0)
C44.0 Skin of lip, NOS 75 ( 0.2) 357 ( 0.2)
C44.1 Eyelid 120 ( 0.3) 615 ( 0.3)
C44.2 External ear 1129 ( 2.8) 5782 ( 2.9)
C44.3 Skin of ear and unspecified parts of face 3518 ( 8.6) 18335 ( 9.1)
C44.4 Skin of scalp and neck 3165 ( 7.8) 15842 ( 7.9)
C44.5 Skin of trunk 12136 ( 29.8) 59770 ( 29.7)
C44.6 Skin of upper limb and shoulder 10107 ( 24.8) 49284 ( 24.5)
C44.7 Skin of lower limb and hip 7696 ( 18.9) 36379 ( 18.1)
C44.8 Overlapping lesion of skin 50 ( 0.1) 230 ( 0.1)
C44.9 Skin, NOS 2474 ( 6.1) 13105 ( 6.5)
C51.0 Labium majus 14 ( 0.0) 98 ( 0.0)
C51.1 Labium minus 14 ( 0.0) 81 ( 0.0)
C51.2 Clitoris 2 ( 0.0) 45 ( 0.0)
C51.8 Overlapping lesion of vulva 8 ( 0.0) 36 ( 0.0)
C51.9 Vulva, NOS 177 ( 0.4) 888 ( 0.4)
C52.9 Vagina, NOS 67 ( 0.2) 347 ( 0.2)
C60.0 Prepuce 1 ( 0.0) 7 ( 0.0)
C60.1 Glans penis 2 ( 0.0) 11 ( 0.0)
C60.2 Body of penis 0 ( 0.0) 2 ( 0.0)
C60.8 Overlapping lesion of penis 0 ( 0.0) 1 ( 0.0)
C60.9 Penis 5 ( 0.0) 24 ( 0.0)

p_table(data,
        vars = c("YEAR_OF_DIAGNOSIS"),
        strata = c("MEDICAID_EXPN_CODE"))
level Non-Expansion State Jan 2014 Expansion States Early Expansion States (2010-13) Late Expansion States (> Jan 2014) Suppressed for Ages 0 - 39 p test
n 83879 71602 43372 32294 37942
YEAR_OF_DIAGNOSIS (%) 2004 5368 ( 6.4) 4616 ( 6.4) 2754 ( 6.3) 1982 ( 6.1) 3036 (8.0) NaN
2005 5840 ( 7.0) 4915 ( 6.9) 3025 ( 7.0) 2164 ( 6.7) 3325 (8.8)
2006 5832 ( 7.0) 5282 ( 7.4) 3005 ( 6.9) 2186 ( 6.8) 3179 (8.4)
2007 6277 ( 7.5) 5295 ( 7.4) 3169 ( 7.3) 2255 ( 7.0) 3224 (8.5)
2008 6620 ( 7.9) 5595 ( 7.8) 3333 ( 7.7) 2315 ( 7.2) 3190 (8.4)
2009 6858 ( 8.2) 5757 ( 8.0) 3466 ( 8.0) 2616 ( 8.1) 3241 (8.5)
2010 6960 ( 8.3) 5865 ( 8.2) 3462 ( 8.0) 2631 ( 8.1) 3049 (8.0)
2011 7247 ( 8.6) 6339 ( 8.9) 3779 ( 8.7) 2795 ( 8.7) 3005 (7.9)
2012 7578 ( 9.0) 6453 ( 9.0) 3851 ( 8.9) 2957 ( 9.2) 3110 (8.2)
2013 8094 ( 9.6) 6782 ( 9.5) 4276 ( 9.9) 3170 ( 9.8) 3083 (8.1)
2014 8214 ( 9.8) 7087 ( 9.9) 4460 (10.3) 3501 (10.8) 3207 (8.5)
2015 8991 (10.7) 7616 (10.6) 4792 (11.0) 3722 (11.5) 3293 (8.7)
2016 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 (0.0)
preExpMedicare  <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion" & INSURANCE_F == "Medicare"))
postExpMedicare <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion" & INSURANCE_F == "Medicare"))
# p = 0.25 when comparing change in proportion of patients with Medicare before and after ACA expansion
prop.test(c(preExpMedicare, postExpMedicare), 
          c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))

    2-sample test for equality of proportions with continuity correction

data:  c(preExpMedicare, postExpMedicare) out of c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% c(preExpMedicare, postExpMedicare) out of     filter(EXPN_GROUP == "Post-Expansion")))
X-squared = 7.8211, df = 1, p-value = 0.005164
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.012912480 -0.002252538
sample estimates:
   prop 1    prop 2 
0.3934805 0.4010630 
preExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion") %>% 
                            filter(INSURANCE_F == "None"))
postExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion") %>% 
                             filter(INSURANCE_F == "None"))
# Significant decrease in the overall proportion of patients without insurance after ACA expansion 
prop.test(c(preExpNoInsurance, postExpNoInsurance), 
          c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))

    2-sample test for equality of proportions with continuity correction

data:  c(preExpNoInsurance, postExpNoInsurance) out of c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% c(preExpNoInsurance, postExpNoInsurance) out of     filter(EXPN_GROUP == "Post-Expansion")))
X-squared = 205.43, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
 0.01108259 0.01393125
sample estimates:
    prop 1     prop 2 
0.02756172 0.01505480 
p_table(no_Excludes, strata = "EXPN_GROUP", vars = "DX_RX_STARTED_DAYS")

level Post-Expansion Pre-Expansion p test
n 40765 201262
DX_RX_STARTED_DAYS (mean (sd)) 12.48 (26.15) 11.03 (28.24) <0.001

data <- data %>% mutate(Insured = INSURANCE_F != "Unknown")

Kaplan Meier Analysis

All

uni_var(test_var = "All", data_imp = data)
_________________________________________________
   
## All
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ All, data = data)

      n  events  median 0.95LCL 0.95UCL 
 269089   58360      NA      NA      NA 

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ All, data = data)

 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 225082   15378    0.940 0.000473        0.939        0.941
   24 193676   11650    0.889 0.000640        0.888        0.890
   36 160798    8870    0.845 0.000757        0.844        0.847
   48 133228    6244    0.810 0.000847        0.808        0.812
   60 108250    4574    0.780 0.000926        0.778        0.782
  120  26477   10347    0.662 0.001397        0.659        0.665



   
## Univariable Cox Proportional Hazard Model for:  All

[1] "Only one level, no Cox model performed"




   
## Unadjusted Kaplan Meier Overall Survival Curve for:  All

Facility Type

uni_var(test_var = "FACILITY_TYPE_F", data_imp = data)
_________________________________________________
   
## FACILITY_TYPE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F, data = data)

   37942 observations deleted due to missingness 
                                                   n events median 0.95LCL 0.95UCL
FACILITY_TYPE_F=Community Cancer Program       15035   4420    150     144      NA
FACILITY_TYPE_F=Comprehensive Comm Ca Program  82869  21864    162     160      NA
FACILITY_TYPE_F=Academic/Research Program     105617  22457     NA     165      NA
FACILITY_TYPE_F=Integrated Network Ca Program  27626   6826    163     163      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F, data = data)

37942 observations deleted due to missingness 
                FACILITY_TYPE_F=Community Cancer Program 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  12124    1418    0.900 0.00251        0.896        0.905
   24  10268     863    0.834 0.00319        0.828        0.840
   36   8410     627    0.780 0.00365        0.772        0.787
   48   6939     426    0.737 0.00398        0.730        0.745
   60   5612     315    0.701 0.00428        0.693        0.710
  120   1343     682    0.567 0.00610        0.555        0.579

                FACILITY_TYPE_F=Comprehensive Comm Ca Program 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  68785    6145    0.922 0.000954        0.920        0.924
   24  59006    4221    0.863 0.001253        0.861        0.866
   36  49104    3150    0.814 0.001455        0.811        0.817
   48  40913    2276    0.774 0.001609        0.771        0.777
   60  33635    1678    0.740 0.001741        0.737        0.743
  120   8201    3854    0.608 0.002522        0.603        0.613

                FACILITY_TYPE_F=Academic/Research Program 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  88530    5217    0.948 0.000708        0.946        0.949
   24  75611    4692    0.895 0.001002        0.893        0.897
   36  61822    3596    0.849 0.001208        0.847        0.852
   48  50290    2543    0.811 0.001368        0.809        0.814
   60  40065    1840    0.779 0.001509        0.776        0.782
  120   9247    4083    0.650 0.002369        0.645        0.654

                FACILITY_TYPE_F=Integrated Network Ca Program 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  22951    1908    0.927 0.00160        0.924        0.930
   24  19701    1297    0.873 0.00211        0.869        0.877
   36  16285    1045    0.823 0.00248        0.818        0.828
   48  13399     684    0.786 0.00275        0.781        0.792
   60  10806     503    0.754 0.00299        0.748        0.760
  120   2525    1244    0.617 0.00453        0.608        0.626




   
## Univariable Cox Proportional Hazard Model for:  FACILITY_TYPE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F, data = data)

  n= 231147, number of events= 55567 
   (37942 observations deleted due to missingness)

                                                 coef exp(coef) se(coef)       z Pr(>|z|)    
FACILITY_TYPE_FComprehensive Comm Ca Program -0.15995   0.85218  0.01649  -9.699   <2e-16 ***
FACILITY_TYPE_FAcademic/Research Program     -0.34989   0.70477  0.01646 -21.263   <2e-16 ***
FACILITY_TYPE_FIntegrated Network Ca Program -0.20951   0.81098  0.01931 -10.852   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                             exp(coef) exp(-coef) lower .95 upper .95
FACILITY_TYPE_FComprehensive Comm Ca Program    0.8522      1.173    0.8251    0.8802
FACILITY_TYPE_FAcademic/Research Program        0.7048      1.419    0.6824    0.7279
FACILITY_TYPE_FIntegrated Network Ca Program    0.8110      1.233    0.7809    0.8423

Concordance= 0.535  (se = 0.001 )
Rsquare= 0.003   (max possible= 0.996 )
Likelihood ratio test= 658.4  on 3 df,   p=0
Wald test            = 666.5  on 3 df,   p=0
Score (logrank) test = 670  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_TYPE_F

Facility Location

uni_var(test_var = "FACILITY_LOCATION_F", data_imp = data)
_________________________________________________
   
## FACILITY_LOCATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_LOCATION_F, data = data)

   37942 observations deleted due to missingness 
                                           n events median 0.95LCL 0.95UCL
FACILITY_LOCATION_F=New England        14633   3342     NA      NA      NA
FACILITY_LOCATION_F=Middle Atlantic    37321   8205    165     163      NA
FACILITY_LOCATION_F=South Atlantic     52385  13223    162     158      NA
FACILITY_LOCATION_F=East North Central 39474   9551    165     165      NA
FACILITY_LOCATION_F=East South Central 15651   4333    149     143      NA
FACILITY_LOCATION_F=West North Central 19556   4343     NA      NA      NA
FACILITY_LOCATION_F=West South Central 12471   3399    161     156      NA
FACILITY_LOCATION_F=Mountain           11965   2772    161     161      NA
FACILITY_LOCATION_F=Pacific            27691   6399     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_LOCATION_F, data = data)

37942 observations deleted due to missingness 
                FACILITY_LOCATION_F=New England 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  12362     820    0.941 0.00200        0.937        0.945
   24  10676     645    0.890 0.00272        0.885        0.895
   36   8880     523    0.843 0.00325        0.837        0.850
   48   7366     349    0.808 0.00363        0.801        0.815
   60   5884     280    0.775 0.00399        0.767        0.782
  120   1375     639    0.639 0.00622        0.627        0.652

                FACILITY_LOCATION_F=Middle Atlantic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  31658    2130    0.940 0.00126        0.937        0.942
   24  26863    1724    0.886 0.00173        0.883        0.890
   36  21828    1249    0.842 0.00205        0.838        0.846
   48  17484     910    0.803 0.00232        0.799        0.808
   60  13636     634    0.771 0.00256        0.766        0.776
  120   2666    1398    0.637 0.00420        0.628        0.645

                FACILITY_LOCATION_F=South Atlantic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  43103    3351    0.932 0.00113        0.930        0.935
   24  36821    2577    0.874 0.00154        0.871        0.877
   36  30686    1969    0.824 0.00181        0.821        0.828
   48  25384    1460    0.783 0.00203        0.779        0.786
   60  20756    1040    0.748 0.00220        0.744        0.752
  120   5032    2498    0.607 0.00327        0.601        0.614

                FACILITY_LOCATION_F=East North Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  32964    2509    0.933 0.00129        0.930        0.936
   24  28267    1867    0.878 0.00174        0.875        0.881
   36  23187    1450    0.830 0.00205        0.826        0.834
   48  19034    1010    0.791 0.00229        0.786        0.795
   60  15352     752    0.757 0.00251        0.752        0.762
  120   3747    1742    0.623 0.00375        0.616        0.631

                FACILITY_LOCATION_F=East South Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  13144    1117    0.925 0.00216        0.921        0.929
   24  11125     887    0.860 0.00291        0.855        0.866
   36   9212     636    0.808 0.00339        0.801        0.815
   48   7578     489    0.762 0.00378        0.755        0.770
   60   6070     346    0.725 0.00410        0.717        0.733
  120   1387     747    0.583 0.00605        0.571        0.595

                FACILITY_LOCATION_F=West North Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  16079    1128    0.939 0.00178        0.935        0.942
   24  13835     858    0.886 0.00241        0.882        0.891
   36  11283     712    0.837 0.00290        0.832        0.843
   48   9254     449    0.801 0.00324        0.795        0.808
   60   7450     327    0.770 0.00353        0.763        0.777
  120   1726     765    0.643 0.00544        0.633        0.654

                FACILITY_LOCATION_F=West South Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  10367    1065    0.911 0.00261        0.906        0.916
   24   8722     676    0.848 0.00336        0.842        0.855
   36   7235     491    0.798 0.00386        0.790        0.805
   48   6000     339    0.758 0.00424        0.749        0.766
   60   4868     264    0.722 0.00458        0.713        0.731
  120   1002     511    0.600 0.00661        0.587        0.613

                FACILITY_LOCATION_F=Mountain 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   9911     775    0.932 0.00238        0.927        0.936
   24   8506     548    0.878 0.00316        0.872        0.884
   36   6977     430    0.830 0.00373        0.823        0.838
   48   5737     301    0.792 0.00417        0.784        0.800
   60   4766     184    0.764 0.00449        0.756        0.773
  120   1181     452    0.646 0.00675        0.633        0.659

                FACILITY_LOCATION_F=Pacific 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  22802    1793    0.932 0.00156        0.929        0.935
   24  19771    1291    0.877 0.00208        0.873        0.881
   36  16333     958    0.831 0.00244        0.827        0.836
   48  13704     622    0.798 0.00269        0.792        0.803
   60  11336     509    0.766 0.00293        0.760        0.771
  120   3200    1111    0.652 0.00418        0.644        0.660




   
## Univariable Cox Proportional Hazard Model for:  FACILITY_LOCATION_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_LOCATION_F, data = data)

  n= 231147, number of events= 55567 
   (37942 observations deleted due to missingness)

                                           coef exp(coef)  se(coef)     z Pr(>|z|)    
FACILITY_LOCATION_FMiddle Atlantic    9.679e-03 1.010e+00 2.052e-02 0.472 0.637193    
FACILITY_LOCATION_FSouth Atlantic     1.178e-01 1.125e+00 1.936e-02 6.086 1.16e-09 ***
FACILITY_LOCATION_FEast North Central 7.440e-02 1.077e+00 2.010e-02 3.702 0.000214 ***
FACILITY_LOCATION_FEast South Central 2.126e-01 1.237e+00 2.302e-02 9.235  < 2e-16 ***
FACILITY_LOCATION_FWest North Central 8.590e-03 1.009e+00 2.301e-02 0.373 0.708930    
FACILITY_LOCATION_FWest South Central 2.152e-01 1.240e+00 2.436e-02 8.834  < 2e-16 ***
FACILITY_LOCATION_FMountain           2.608e-02 1.026e+00 2.569e-02 1.015 0.310105    
FACILITY_LOCATION_FPacific            1.576e-05 1.000e+00 2.134e-02 0.001 0.999411    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                      exp(coef) exp(-coef) lower .95 upper .95
FACILITY_LOCATION_FMiddle Atlantic        1.010     0.9904    0.9699     1.051
FACILITY_LOCATION_FSouth Atlantic         1.125     0.8889    1.0832     1.169
FACILITY_LOCATION_FEast North Central     1.077     0.9283    1.0356     1.121
FACILITY_LOCATION_FEast South Central     1.237     0.8085    1.1823     1.294
FACILITY_LOCATION_FWest North Central     1.009     0.9914    0.9641     1.055
FACILITY_LOCATION_FWest South Central     1.240     0.8064    1.1823     1.301
FACILITY_LOCATION_FMountain               1.026     0.9743    0.9760     1.079
FACILITY_LOCATION_FPacific                1.000     1.0000    0.9590     1.043

Concordance= 0.519  (se = 0.001 )
Rsquare= 0.001   (max possible= 0.996 )
Likelihood ratio test= 283.7  on 8 df,   p=0
Wald test            = 288.6  on 8 df,   p=0
Score (logrank) test = 289.3  on 8 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_LOCATION_F

Facility Geography

uni_var(test_var = "FACILITY_GEOGRAPHY", data_imp = data)
_________________________________________________
   
## FACILITY_GEOGRAPHY
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_GEOGRAPHY, data = data)

   37942 observations deleted due to missingness 
                                 n events median 0.95LCL 0.95UCL
FACILITY_GEOGRAPHY=Northeast 51954  11547     NA     165      NA
FACILITY_GEOGRAPHY=South     64856  16622    162     159      NA
FACILITY_GEOGRAPHY=Midwest   74681  18227    165     164      NA
FACILITY_GEOGRAPHY=West      39656   9171     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_GEOGRAPHY, data = data)

37942 observations deleted due to missingness 
                FACILITY_GEOGRAPHY=Northeast 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  44020    2950    0.940 0.00107        0.938        0.942
   24  37539    2369    0.887 0.00146        0.884        0.890
   36  30708    1772    0.842 0.00174        0.839        0.846
   48  24850    1259    0.805 0.00195        0.801        0.809
   60  19520     914    0.772 0.00215        0.768        0.776
  120   4041    2037    0.637 0.00348        0.631        0.644

                FACILITY_GEOGRAPHY=South 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  53470    4416    0.928 0.00104        0.926        0.930
   24  45543    3253    0.869 0.00140        0.866        0.872
   36  37921    2460    0.819 0.00164        0.816        0.822
   48  31384    1799    0.778 0.00183        0.774        0.781
   60  25624    1304    0.743 0.00199        0.739        0.747
  120   6034    3009    0.606 0.00294        0.600        0.611

                FACILITY_GEOGRAPHY=Midwest 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  62187    4754    0.933 0.000943        0.931        0.935
   24  53227    3612    0.876 0.001271        0.874        0.879
   36  43682    2798    0.827 0.001503        0.824        0.830
   48  35866    1948    0.787 0.001679        0.784        0.791
   60  28872    1425    0.753 0.001833        0.750        0.757
  120   6860    3254    0.620 0.002752        0.614        0.625

                FACILITY_GEOGRAPHY=West 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  32713    2568    0.932 0.00130        0.929        0.934
   24  28277    1839    0.877 0.00174        0.874        0.881
   36  23310    1388    0.831 0.00204        0.827        0.835
   48  19441     923    0.796 0.00226        0.791        0.800
   60  16102     693    0.765 0.00246        0.760        0.770
  120   4381    1563    0.650 0.00355        0.643        0.657




   
## Univariable Cox Proportional Hazard Model for:  FACILITY_GEOGRAPHY

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_GEOGRAPHY, data = data)

  n= 231147, number of events= 55567 
   (37942 observations deleted due to missingness)

                               coef exp(coef)  se(coef)      z Pr(>|z|)    
FACILITY_GEOGRAPHYSouth   0.1301286 1.1389748 0.0121170 10.739  < 2e-16 ***
FACILITY_GEOGRAPHYMidwest 0.0821252 1.0855917 0.0118955  6.904 5.06e-12 ***
FACILITY_GEOGRAPHYWest    0.0009841 1.0009845 0.0139936  0.070    0.944    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                          exp(coef) exp(-coef) lower .95 upper .95
FACILITY_GEOGRAPHYSouth       1.139     0.8780    1.1122     1.166
FACILITY_GEOGRAPHYMidwest     1.086     0.9212    1.0606     1.111
FACILITY_GEOGRAPHYWest        1.001     0.9990    0.9739     1.029

Concordance= 0.515  (se = 0.001 )
Rsquare= 0.001   (max possible= 0.996 )
Likelihood ratio test= 162.6  on 3 df,   p=0
Wald test            = 162.2  on 3 df,   p=0
Score (logrank) test = 162.3  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_GEOGRAPHY

Age Group

uni_var(test_var = "AGE_F", data_imp = data)
_________________________________________________
   
## AGE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_F, data = data)

   34 observations deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
AGE_F=(0,54]   107677  11031     NA      NA      NA
AGE_F=(54,64]   60646   9824     NA      NA      NA
AGE_F=(64,74]   51176  12284  149.8   146.5   153.3
AGE_F=(74,100]  49556  25217   60.6    59.7    61.5

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_F, data = data)

34 observations deleted due to missingness 
                AGE_F=(0,54] 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  92193    3053    0.970 0.000537        0.969        0.971
   24  81724    2343    0.944 0.000741        0.943        0.946
   36  70527    1688    0.923 0.000880        0.922        0.925
   48  60487    1190    0.907 0.000988        0.905        0.909
   60  50712     828    0.893 0.001077        0.891        0.896
  120  14310    1733    0.847 0.001545        0.844        0.850

                AGE_F=(54,64] 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  50732    2864    0.950 0.000911        0.948        0.952
   24  44074    1957    0.912 0.001220        0.909        0.914
   36  36713    1573    0.877 0.001457        0.874        0.880
   48  30492    1007    0.851 0.001627        0.848        0.854
   60  24862     688    0.830 0.001772        0.826        0.833
  120   6092    1533    0.747 0.002700        0.741        0.752

                AGE_F=(64,74] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  42621    3135    0.935 0.00112        0.933        0.938
   24  36321    2309    0.882 0.00150        0.879        0.885
   36  29455    1809    0.835 0.00179        0.832        0.839
   48  23906    1297    0.795 0.00202        0.791        0.799
   60  19007     926    0.762 0.00222        0.757        0.766
  120   4040    2430    0.598 0.00367        0.591        0.605

                AGE_F=(74,100] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  39511    6325    0.867 0.00156        0.864        0.870
   24  31538    5040    0.752 0.00203        0.748        0.756
   36  24086    3799    0.656 0.00230        0.651        0.660
   48  18329    2750    0.575 0.00248        0.570        0.580
   60  13658    2131    0.503 0.00262        0.498        0.508
  120   2030    4651    0.240 0.00326        0.233        0.246




   
## Univariable Cox Proportional Hazard Model for:  AGE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_F, data = data)

  n= 269055, number of events= 58356 
   (34 observations deleted due to missingness)

                 coef exp(coef) se(coef)      z Pr(>|z|)    
AGE_F(54,64]  0.54866   1.73093  0.01388  39.54   <2e-16 ***
AGE_F(64,74]  1.00006   2.71844  0.01313  76.14   <2e-16 ***
AGE_F(74,100] 1.91851   6.81077  0.01149 166.95   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
AGE_F(54,64]      1.731     0.5777     1.684     1.779
AGE_F(64,74]      2.718     0.3679     2.649     2.789
AGE_F(74,100]     6.811     0.1468     6.659     6.966

Concordance= 0.68  (se = 0.001 )
Rsquare= 0.113   (max possible= 0.994 )
Likelihood ratio test= 32402  on 3 df,   p=0
Wald test            = 32768  on 3 df,   p=0
Score (logrank) test = 40304  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  AGE_F

Age Group

uni_var(test_var = "AGE_40", data_imp = data)
_________________________________________________
   
## AGE_40
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_40, data = data)

   34 observations deleted due to missingness 
                     n events median 0.95LCL 0.95UCL
AGE_40=(0,40]    41135   3069     NA      NA      NA
AGE_40=(40,100] 227920  55287    165     163      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_40, data = data)

34 observations deleted due to missingness 
                AGE_40=(0,40] 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  35467     769    0.980 0.000713        0.979        0.981
   24  31565     642    0.961 0.001008        0.959        0.963
   36  27335     482    0.946 0.001217        0.943        0.948
   48  23535     355    0.933 0.001386        0.930        0.935
   60  19696     254    0.922 0.001528        0.919        0.925
  120   5623     526    0.884 0.002261        0.880        0.888

                AGE_40=(40,100] 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 189590   14608    0.932 0.000541        0.931        0.933
   24 162092   11007    0.876 0.000729        0.874        0.877
   36 133446    8387    0.827 0.000860        0.826        0.829
   48 109679    5889    0.788 0.000959        0.786        0.790
   60  88543    4319    0.754 0.001047        0.752        0.757
  120  20849    9821    0.621 0.001579        0.618        0.624




   
## Univariable Cox Proportional Hazard Model for:  AGE_40

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_40, data = data)

  n= 269055, number of events= 58356 
   (34 observations deleted due to missingness)

                  coef exp(coef) se(coef)     z Pr(>|z|)    
AGE_40(40,100] 1.30584   3.69078  0.01855 70.39   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

               exp(coef) exp(-coef) lower .95 upper .95
AGE_40(40,100]     3.691     0.2709     3.559     3.827

Concordance= 0.555  (se = 0.001 )
Rsquare= 0.027   (max possible= 0.994 )
Likelihood ratio test= 7421  on 1 df,   p=0
Wald test            = 4955  on 1 df,   p=0
Score (logrank) test = 5699  on 1 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  AGE_40

Gender

uni_var(test_var = "SEX_F", data_imp = data)
_________________________________________________
   
## SEX_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SEX_F, data = data)

                  n events median 0.95LCL 0.95UCL
SEX_F=Male   147088  37682    164     162      NA
SEX_F=Female 122001  20678     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SEX_F, data = data)

                SEX_F=Male 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 121716   10085    0.928 0.000695        0.926        0.929
   24 103537    7549    0.868 0.000933        0.866        0.869
   36  84929    5734    0.816 0.001098        0.814        0.818
   48  69534    4065    0.774 0.001223        0.772        0.777
   60  55969    2953    0.739 0.001332        0.736        0.741
  120  13208    6538    0.602 0.001975        0.598        0.606

                SEX_F=Female 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 103366    5293    0.954 0.000617        0.953        0.955
   24  90139    4101    0.915 0.000846        0.913        0.916
   36  75869    3136    0.881 0.001009        0.879        0.883
   48  63694    2179    0.853 0.001133        0.851        0.856
   60  52281    1621    0.830 0.001245        0.827        0.832
  120  13269    3809    0.734 0.001921        0.730        0.738




   
## Univariable Cox Proportional Hazard Model for:  SEX_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SEX_F, data = data)

  n= 269089, number of events= 58360 

                 coef exp(coef)  se(coef)      z Pr(>|z|)    
SEX_FFemale -0.481575  0.617810  0.008657 -55.63   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

            exp(coef) exp(-coef) lower .95 upper .95
SEX_FFemale    0.6178      1.619    0.6074    0.6284

Concordance= 0.557  (se = 0.001 )
Rsquare= 0.012   (max possible= 0.994 )
Likelihood ratio test= 3214  on 1 df,   p=0
Wald test            = 3094  on 1 df,   p=0
Score (logrank) test = 3155  on 1 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SEX_F

RACE_F

uni_var(test_var = "RACE_F", data_imp = data)
_________________________________________________
   
## RACE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RACE_F, data = data)

                      n events median 0.95LCL 0.95UCL
RACE_F=White     261259  56388     NA      NA      NA
RACE_F=Black       1814    774   72.5    63.3    86.3
RACE_F=Other/Unk   5100    939     NA      NA      NA
RACE_F=Asian        916    259     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RACE_F, data = data)

                RACE_F=White 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 218800   14767    0.940 0.000477        0.939        0.941
   24 188370   11217    0.890 0.000647        0.889        0.891
   36 156443    8572    0.847 0.000766        0.845        0.848
   48 129626    6055    0.812 0.000857        0.810        0.813
   60 105308    4434    0.781 0.000938        0.780        0.783
  120  25670   10075    0.663 0.001420        0.660        0.666

                RACE_F=Black 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1347     301    0.826 0.00913        0.808        0.844
   24   1070     163    0.723 0.01102        0.701        0.745
   36    838     101    0.650 0.01207        0.626        0.674
   48    653      74    0.588 0.01288        0.563        0.614
   60    499      50    0.540 0.01354        0.514        0.567
  120    108      75    0.417 0.01704        0.385        0.452

                RACE_F=Other/Unk 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4212     222    0.953 0.00308        0.947        0.959
   24   3654     200    0.906 0.00438        0.897        0.914
   36   3067     153    0.865 0.00528        0.855        0.876
   48   2598      93    0.837 0.00585        0.826        0.849
   60   2170      76    0.811 0.00640        0.798        0.823
  120    628     177    0.711 0.00930        0.693        0.730

                RACE_F=Asian 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    723      88    0.898  0.0103        0.878        0.918
   24    582      70    0.806  0.0140        0.779        0.834
   36    450      44    0.739  0.0160        0.709        0.772
   48    351      22    0.700  0.0173        0.667        0.735
   60    273      14    0.670  0.0183        0.635        0.706
  120     71      20    0.599  0.0228        0.556        0.645




   
## Univariable Cox Proportional Hazard Model for:  RACE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RACE_F, data = data)

  n= 269089, number of events= 58360 

                    coef exp(coef) se(coef)      z Pr(>|z|)    
RACE_FBlack      0.90375   2.46885  0.03619 24.969  < 2e-16 ***
RACE_FOther/Unk -0.18161   0.83392  0.03291 -5.519 3.40e-08 ***
RACE_FAsian      0.41481   1.51408  0.06228  6.660 2.73e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
RACE_FBlack        2.4688     0.4050    2.2998    2.6503
RACE_FOther/Unk    0.8339     1.1992    0.7818    0.8895
RACE_FAsian        1.5141     0.6605    1.3401    1.7107

Concordance= 0.507  (se = 0 )
Rsquare= 0.002   (max possible= 0.994 )
Likelihood ratio test= 547.6  on 3 df,   p=0
Wald test            = 700.5  on 3 df,   p=0
Score (logrank) test = 745.5  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RACE_F

Hispanic

uni_var(test_var = "HISPANIC", data_imp = data)
_________________________________________________
   
## HISPANIC
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISPANIC, data = data)

                      n events median 0.95LCL 0.95UCL
HISPANIC=No      249614  53566     NA      NA      NA
HISPANIC=Yes       4270   1064     NA      NA      NA
HISPANIC=Unknown  15205   3730     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISPANIC, data = data)

                HISPANIC=No 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 208776   14166    0.940 0.000489        0.939        0.941
   24 179321   10766    0.889 0.000663        0.888        0.891
   36 148375    8180    0.846 0.000786        0.844        0.848
   48 122386    5761    0.811 0.000881        0.809        0.812
   60  98924    4208    0.780 0.000964        0.778        0.782
  120  23660    9338    0.663 0.001466        0.660        0.665

                HISPANIC=Yes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3411     351    0.912 0.00448        0.904        0.921
   24   2822     243    0.844 0.00591        0.832        0.856
   36   2269     173    0.788 0.00689        0.775        0.802
   48   1825     112    0.746 0.00758        0.731        0.761
   60   1443      61    0.719 0.00807        0.703        0.735
  120    323     111    0.631 0.01098        0.610        0.653

                HISPANIC=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  12895     861    0.940 0.00197        0.937        0.944
   24  11533     641    0.892 0.00263        0.887        0.897
   36  10154     517    0.851 0.00308        0.845        0.857
   48   9017     371    0.818 0.00339        0.812        0.825
   60   7883     305    0.789 0.00366        0.782        0.796
  120   2494     898    0.663 0.00509        0.653        0.673




   
## Univariable Cox Proportional Hazard Model for:  HISPANIC

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISPANIC, data = data)

  n= 269089, number of events= 58360 

                    coef exp(coef) se(coef)      z Pr(>|z|)    
HISPANICYes      0.24422   1.27662  0.03096  7.888 3.11e-15 ***
HISPANICUnknown -0.01566   0.98446  0.01695 -0.924    0.356    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
HISPANICYes        1.2766     0.7833    1.2015     1.356
HISPANICUnknown    0.9845     1.0158    0.9523     1.018

Concordance= 0.503  (se = 0.001 )
Rsquare= 0   (max possible= 0.994 )
Likelihood ratio test= 59  on 2 df,   p=1.545e-13
Wald test            = 63.67  on 2 df,   p=1.499e-14
Score (logrank) test = 63.98  on 2 df,   p=1.277e-14
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  HISPANIC

Insurance Status

uni_var(test_var = "INSURANCE_F", data_imp = data)
_________________________________________________
   
## INSURANCE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ INSURANCE_F, data = data)

                                  n events median 0.95LCL 0.95UCL
INSURANCE_F=Private          158775  18823     NA      NA      NA
INSURANCE_F=None               7722   2059     NA      NA      NA
INSURANCE_F=Medicaid           7837   2369     NA   139.6      NA
INSURANCE_F=Medicare          91803  34515     93    91.9    94.5
INSURANCE_F=Other Government   2952    594     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ INSURANCE_F, data = data)

                INSURANCE_F=Private 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 135884    4548    0.970 0.000445        0.969        0.970
   24 120193    3810    0.941 0.000626        0.940        0.942
   36 102754    3053    0.916 0.000761        0.914        0.917
   48  87253    2085    0.896 0.000860        0.894        0.897
   60  72542    1551    0.879 0.000949        0.877        0.880
  120  19583    3349    0.815 0.001438        0.812        0.817

                INSURANCE_F=None 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   6059     772    0.894 0.00361        0.887        0.901
   24   5110     425    0.829 0.00454        0.820        0.838
   36   4259     291    0.779 0.00512        0.769        0.789
   48   3520     209    0.738 0.00558        0.727        0.749
   60   2796     112    0.712 0.00588        0.701        0.724
  120    662     232    0.620 0.00797        0.605        0.636

                INSURANCE_F=Medicaid 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   5920    1015    0.863 0.00401        0.855        0.871
   24   4632     518    0.783 0.00495        0.773        0.793
   36   3482     304    0.726 0.00556        0.715        0.737
   48   2732     181    0.685 0.00604        0.673        0.697
   60   2126     106    0.656 0.00642        0.643        0.668
  120    462     225    0.547 0.00900        0.530        0.565

                INSURANCE_F=Medicare 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  74826    8845    0.899 0.00102        0.897        0.901
   24  61766    6750    0.815 0.00135        0.812        0.817
   36  48712    5132    0.742 0.00156        0.739        0.745
   48  38460    3712    0.681 0.00173        0.678        0.685
   60  29794    2780    0.628 0.00187        0.624        0.631
  120   5570    6468    0.409 0.00272        0.404        0.415

                INSURANCE_F=Other Government 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2393     198    0.928 0.00493        0.918        0.938
   24   1975     147    0.868 0.00668        0.855        0.881
   36   1591      90    0.825 0.00773        0.810        0.840
   48   1263      57    0.792 0.00855        0.776        0.809
   60    992      25    0.775 0.00905        0.757        0.793
  120    200      73    0.678 0.01424        0.651        0.707




   
## Univariable Cox Proportional Hazard Model for:  INSURANCE_F
X matrix deemed to be singular; variable 5
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ INSURANCE_F, data = data)

  n= 269089, number of events= 58360 

                                coef exp(coef) se(coef)      z Pr(>|z|)    
INSURANCE_FNone             0.976034  2.653909 0.023216  42.04   <2e-16 ***
INSURANCE_FMedicaid         1.251244  3.494687 0.021820  57.34   <2e-16 ***
INSURANCE_FMedicare         1.357996  3.888393 0.009098 149.26   <2e-16 ***
INSURANCE_FOther Government 0.718446  2.051243 0.041679  17.24   <2e-16 ***
INSURANCE_FUnknown                NA        NA 0.000000     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                            exp(coef) exp(-coef) lower .95 upper .95
INSURANCE_FNone                 2.654     0.3768     2.536     2.777
INSURANCE_FMedicaid             3.495     0.2861     3.348     3.647
INSURANCE_FMedicare             3.888     0.2572     3.820     3.958
INSURANCE_FOther Government     2.051     0.4875     1.890     2.226
INSURANCE_FUnknown                 NA         NA        NA        NA

Concordance= 0.654  (se = 0.001 )
Rsquare= 0.087   (max possible= 0.994 )
Likelihood ratio test= 24472  on 4 df,   p=0
Wald test            = 22619  on 4 df,   p=0
Score (logrank) test = 26080  on 4 df,   p=0
Removed 2 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  INSURANCE_F

Overall Survival pre/post-ACA expansion

uni_var(test_var = "EXPN_GROUP", data_imp = no_Excludes)
_________________________________________________
   
## EXPN_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EXPN_GROUP, data = no_Excludes)

                               n events median 0.95LCL 0.95UCL
EXPN_GROUP=Post-Expansion  40765   6213     NA      NA      NA
EXPN_GROUP=Pre-Expansion  201262  55061    164     163      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EXPN_GROUP, data = no_Excludes)

                EXPN_GROUP=Post-Expansion 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  32031    2509    0.933 0.00129        0.931        0.936
   24  23813    1682    0.880 0.00176        0.876        0.883
   36  14017    1011    0.834 0.00219        0.829        0.838
   48   8323     500    0.796 0.00268        0.790        0.801
   60   5196     292    0.762 0.00324        0.755        0.768

                EXPN_GROUP=Pre-Expansion 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 167970   14781    0.923 0.000610        0.922        0.924
   24 146437   10748    0.862 0.000805        0.860        0.864
   36 125987    8094    0.812 0.000929        0.810        0.814
   48 106709    5794    0.773 0.001019        0.771        0.775
   60  87635    4282    0.739 0.001095        0.737        0.742
  120  21945   10058    0.609 0.001556        0.606        0.613




   
## Univariable Cox Proportional Hazard Model for:  EXPN_GROUP

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EXPN_GROUP, data = no_Excludes)

  n= 242027, number of events= 61274 

                           coef exp(coef) se(coef)     z Pr(>|z|)    
EXPN_GROUPPre-Expansion 0.12101   1.12863  0.01357 8.914   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                        exp(coef) exp(-coef) lower .95 upper .95
EXPN_GROUPPre-Expansion     1.129      0.886     1.099     1.159

Concordance= 0.507  (se = 0.001 )
Rsquare= 0   (max possible= 0.997 )
Likelihood ratio test= 81.89  on 1 df,   p=0
Wald test            = 79.46  on 1 df,   p=0
Score (logrank) test = 79.56  on 1 df,   p=0





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  EXPN_GROUP

Education

uni_var(test_var = "EDUCATION_F", data_imp = data)
_________________________________________________
   
## EDUCATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EDUCATION_F, data = data)

   1151 observations deleted due to missingness 
                             n events median 0.95LCL 0.95UCL
EDUCATION_F=21% or more  27806   7905    160     152      NA
EDUCATION_F=13 - 20.9%   58222  14533    164     163      NA
EDUCATION_F=7 - 12.9%    93536  20301     NA      NA      NA
EDUCATION_F=Less than 7% 88374  15418     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EDUCATION_F, data = data)

1151 observations deleted due to missingness 
                EDUCATION_F=21% or more 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  22792    2307    0.913 0.00174        0.909        0.916
   24  19236    1676    0.843 0.00230        0.838        0.847
   36  15785    1182    0.788 0.00265        0.782        0.793
   48  13087     823    0.744 0.00291        0.738        0.750
   60  10610     572    0.709 0.00312        0.703        0.715
  120   2492    1192    0.584 0.00440        0.575        0.592

                EDUCATION_F=13 - 20.9% 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  48392    3908    0.929 0.00109        0.927        0.931
   24  41196    2955    0.870 0.00147        0.867        0.873
   36  34014    2133    0.822 0.00172        0.819        0.825
   48  28033    1541    0.782 0.00191        0.778        0.786
   60  22585    1145    0.747 0.00209        0.743        0.751
  120   5455    2539    0.615 0.00310        0.609        0.621

                EDUCATION_F=7 - 12.9% 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  78314    5291    0.940 0.000798        0.939        0.942
   24  67355    4007    0.890 0.001081        0.888        0.892
   36  55703    3159    0.845 0.001286        0.843        0.848
   48  45996    2196    0.809 0.001442        0.807        0.812
   60  37466    1573    0.779 0.001575        0.776        0.783
  120   9241    3617    0.661 0.002373        0.656        0.665

                EDUCATION_F=Less than 7% 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  74733    3822    0.954 0.000725        0.953        0.956
   24  65237    2968    0.915 0.000995        0.913        0.917
   36  54760    2361    0.879 0.001193        0.877        0.882
   48  45678    1663    0.851 0.001347        0.848        0.853
   60  37251    1266    0.825 0.001487        0.822        0.828
  120   9231    2969    0.720 0.002335        0.715        0.725




   
## Univariable Cox Proportional Hazard Model for:  EDUCATION_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EDUCATION_F, data = data)

  n= 267938, number of events= 58157 
   (1151 observations deleted due to missingness)

                            coef exp(coef) se(coef)      z Pr(>|z|)    
EDUCATION_F13 - 20.9%   -0.14791   0.86251  0.01398 -10.58   <2e-16 ***
EDUCATION_F7 - 12.9%    -0.30804   0.73489  0.01326 -23.23   <2e-16 ***
EDUCATION_FLess than 7% -0.55635   0.57330  0.01383 -40.22   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                        exp(coef) exp(-coef) lower .95 upper .95
EDUCATION_F13 - 20.9%      0.8625      1.159    0.8392    0.8865
EDUCATION_F7 - 12.9%       0.7349      1.361    0.7160    0.7542
EDUCATION_FLess than 7%    0.5733      1.744    0.5580    0.5891

Concordance= 0.555  (se = 0.001 )
Rsquare= 0.008   (max possible= 0.994 )
Likelihood ratio test= 2066  on 3 df,   p=0
Wald test            = 2058  on 3 df,   p=0
Score (logrank) test = 2088  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  EDUCATION_F

Urban/Rural

uni_var(test_var = "U_R_F", data_imp = data)
_________________________________________________
   
## U_R_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ U_R_F, data = data)

   8149 observations deleted due to missingness 
                 n events median 0.95LCL 0.95UCL
U_R_F=Metro 220420  46829     NA      NA      NA
U_R_F=Urban  35956   8676     NA     164      NA
U_R_F=Rural   4564   1214     NA     142      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ U_R_F, data = data)

8149 observations deleted due to missingness 
                U_R_F=Metro 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 184457   12361    0.941 0.000518        0.940        0.942
   24 159141    9272    0.891 0.000700        0.890        0.893
   36 132439    7080    0.849 0.000828        0.847        0.851
   48 109910    5028    0.814 0.000928        0.813        0.816
   60  89333    3702    0.785 0.001015        0.783        0.787
  120  21989    8340    0.669 0.001529        0.666        0.672

                U_R_F=Urban 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  30127    2282    0.933 0.00136        0.930        0.936
   24  25674    1762    0.876 0.00183        0.873        0.880
   36  21064    1350    0.827 0.00216        0.823        0.831
   48  17335     922    0.788 0.00241        0.783        0.793
   60  14075     664    0.755 0.00263        0.750        0.760
  120   3357    1508    0.625 0.00396        0.617        0.632

                U_R_F=Rural 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3794     319    0.926 0.00397        0.919        0.934
   24   3173     261    0.860 0.00542        0.849        0.870
   36   2618     186    0.806 0.00635        0.794        0.819
   48   2110     137    0.760 0.00710        0.747        0.774
   60   1707      83    0.728 0.00764        0.713        0.743
  120    393     201    0.593 0.01126        0.571        0.616




   
## Univariable Cox Proportional Hazard Model for:  U_R_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ U_R_F, data = data)

  n= 260940, number of events= 56719 
   (8149 observations deleted due to missingness)

              coef exp(coef) se(coef)      z Pr(>|z|)    
U_R_FUrban 0.14894   1.16060  0.01169 12.742   <2e-16 ***
U_R_FRural 0.26840   1.30787  0.02907  9.233   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

           exp(coef) exp(-coef) lower .95 upper .95
U_R_FUrban     1.161     0.8616     1.134     1.187
U_R_FRural     1.308     0.7646     1.235     1.385

Concordance= 0.511  (se = 0.001 )
Rsquare= 0.001   (max possible= 0.994 )
Likelihood ratio test= 223.7  on 2 df,   p=0
Wald test            = 233.7  on 2 df,   p=0
Score (logrank) test = 234.5  on 2 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  U_R_F

Class (treatment at performing facility)

uni_var(test_var = "CLASS_OF_CASE_F", data_imp = data)
_________________________________________________
   
## CLASS_OF_CASE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CLASS_OF_CASE_F, data = data)

                                    n events median 0.95LCL 0.95UCL
CLASS_OF_CASE_F=Other_Facility   2578   1231   21.8    19.4    25.4
CLASS_OF_CASE_F=All_Part_Prim  266511  57129     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CLASS_OF_CASE_F, data = data)

                CLASS_OF_CASE_F=Other_Facility 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1093     808    0.616  0.0108        0.595        0.638
   24    710     217    0.484  0.0116        0.462        0.507
   36    507      93    0.414  0.0120        0.391        0.438
   48    364      43    0.374  0.0123        0.351        0.399
   60    279      22    0.349  0.0126        0.326        0.375
  120     48      45    0.252  0.0164        0.222        0.286

                CLASS_OF_CASE_F=All_Part_Prim 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 223989   14570    0.942 0.000465        0.941        0.943
   24 192966   11433    0.892 0.000635        0.891        0.893
   36 160291    8777    0.849 0.000754        0.847        0.850
   48 132864    6201    0.813 0.000845        0.812        0.815
   60 107971    4552    0.783 0.000926        0.781        0.785
  120  26429   10302    0.665 0.001401        0.662        0.668




   
## Univariable Cox Proportional Hazard Model for:  CLASS_OF_CASE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CLASS_OF_CASE_F, data = data)

  n= 269089, number of events= 58360 

                                 coef exp(coef) se(coef)      z Pr(>|z|)    
CLASS_OF_CASE_FAll_Part_Prim -1.67303   0.18768  0.02886 -57.96   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                             exp(coef) exp(-coef) lower .95 upper .95
CLASS_OF_CASE_FAll_Part_Prim    0.1877      5.328    0.1774    0.1986

Concordance= 0.511  (se = 0 )
Rsquare= 0.008   (max possible= 0.994 )
Likelihood ratio test= 2098  on 1 df,   p=0
Wald test            = 3360  on 1 df,   p=0
Score (logrank) test = 4220  on 1 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  CLASS_OF_CASE_F

Year

uni_var(test_var = "YEAR_OF_DIAGNOSIS", data_imp = data)
_________________________________________________
   
## YEAR_OF_DIAGNOSIS
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ YEAR_OF_DIAGNOSIS, data = data)

                           n events median 0.95LCL 0.95UCL
YEAR_OF_DIAGNOSIS=2004 17756   5693     NA      NA      NA
YEAR_OF_DIAGNOSIS=2005 19269   5768     NA   154.8      NA
YEAR_OF_DIAGNOSIS=2006 19484   5845     NA      NA      NA
YEAR_OF_DIAGNOSIS=2007 20220   5632     NA   131.0      NA
YEAR_OF_DIAGNOSIS=2008 21053   5819     NA      NA      NA
YEAR_OF_DIAGNOSIS=2009 21938   5455     NA      NA      NA
YEAR_OF_DIAGNOSIS=2010 21967   5136   95.9    95.5      NA
YEAR_OF_DIAGNOSIS=2011 23165   4991     NA      NA      NA
YEAR_OF_DIAGNOSIS=2012 23949   4338   71.8    71.7      NA
YEAR_OF_DIAGNOSIS=2013 25405   3982     NA      NA      NA
YEAR_OF_DIAGNOSIS=2014 26469   3217     NA      NA      NA
YEAR_OF_DIAGNOSIS=2015 28414   2484     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ YEAR_OF_DIAGNOSIS, data = data)

                YEAR_OF_DIAGNOSIS=2004 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  15516    1034    0.940 0.00182        0.936        0.943
   24  14269     863    0.886 0.00246        0.882        0.891
   36  13302     724    0.841 0.00285        0.835        0.847
   48  12532     536    0.807 0.00310        0.801        0.813
   60  11768     450    0.778 0.00328        0.771        0.784
  120   7573    1546    0.664 0.00388        0.656        0.671

                YEAR_OF_DIAGNOSIS=2005 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  16983    1068    0.943 0.00170        0.939        0.946
   24  15773     884    0.893 0.00229        0.889        0.898
   36  14840     712    0.852 0.00264        0.847        0.858
   48  14067     542    0.821 0.00287        0.816        0.827
   60  13212     484    0.793 0.00305        0.787        0.799
  120   8494    1615    0.684 0.00364        0.677        0.691

                YEAR_OF_DIAGNOSIS=2006 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  17095    1148    0.939 0.00174        0.936        0.942
   24  15838     899    0.889 0.00232        0.884        0.894
   36  14741     824    0.842 0.00271        0.837        0.848
   48  13742     662    0.804 0.00296        0.798        0.810
   60  12837     502    0.774 0.00314        0.768        0.780
  120   7254    1581    0.665 0.00373        0.658        0.672

                YEAR_OF_DIAGNOSIS=2007 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  17630    1220    0.937 0.00174        0.934        0.941
   24  16258     964    0.885 0.00231        0.881        0.890
   36  15216     762    0.844 0.00265        0.838        0.849
   48  14283     589    0.811 0.00288        0.805        0.816
   60  13209     462    0.784 0.00304        0.778        0.790
  120   3155    1570    0.669 0.00384        0.661        0.676

                YEAR_OF_DIAGNOSIS=2008 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  18237    1286    0.937 0.00171        0.933        0.940
   24  16741    1052    0.882 0.00230        0.877        0.886
   36  15556     815    0.838 0.00264        0.833        0.844
   48  14405     646    0.803 0.00287        0.797        0.809
   60  13296     509    0.774 0.00304        0.768        0.780
  120      1    1511    0.605 0.01704        0.572        0.639

                YEAR_OF_DIAGNOSIS=2009 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  18834    1327    0.937 0.00168        0.934        0.940
   24  17263    1044    0.884 0.00224        0.880        0.889
   36  15919     806    0.842 0.00258        0.837        0.847
   48  14816     623    0.809 0.00280        0.803        0.814
   60  13557     527    0.779 0.00298        0.774        0.785

                YEAR_OF_DIAGNOSIS=2010 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  18846    1362    0.935 0.00169        0.932        0.939
   24  17290    1002    0.885 0.00223        0.880        0.889
   36  15993     793    0.844 0.00256        0.839        0.849
   48  14686     613    0.811 0.00279        0.805        0.816
   60  13067     566    0.778 0.00299        0.772        0.784

                YEAR_OF_DIAGNOSIS=2011 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  19678    1341    0.939 0.00161        0.936        0.942
   24  17899    1095    0.886 0.00218        0.882        0.890
   36  16321     857    0.843 0.00253        0.838        0.848
   48  14534     708    0.805 0.00278        0.799        0.810
   60  11729     521    0.774 0.00299        0.768        0.780

                YEAR_OF_DIAGNOSIS=2012 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  20079    1376    0.939 0.00159        0.936        0.942
   24  18148    1003    0.891 0.00211        0.887        0.895
   36  16238     845    0.848 0.00247        0.844        0.853
   48  13481     590    0.816 0.00272        0.810        0.821
   60   5568     397    0.784 0.00306        0.778        0.790

                YEAR_OF_DIAGNOSIS=2013 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  20733    1409    0.941 0.00153        0.938        0.944
   24  18371    1052    0.891 0.00208        0.887        0.895
   36  15290     816    0.849 0.00244        0.845        0.854
   48   6677     549    0.810 0.00289        0.804        0.815
   60      7     156    0.678 0.03851        0.607        0.758

                YEAR_OF_DIAGNOSIS=2014 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  20961    1400    0.943 0.00149        0.940        0.946
   24  17157     968    0.896 0.00203        0.892        0.900
   36   7375     663    0.852 0.00259        0.847        0.857
   48      5     186    0.763 0.01390        0.736        0.790

                YEAR_OF_DIAGNOSIS=2015 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  20490    1407    0.944 0.00145        0.941        0.947
   24   8669     824    0.894 0.00223        0.890        0.898
   36      7     253    0.730 0.03242        0.669        0.797




   
## Univariable Cox Proportional Hazard Model for:  YEAR_OF_DIAGNOSIS
X matrix deemed to be singular; variable 12
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ YEAR_OF_DIAGNOSIS, data = data)

  n= 269089, number of events= 58360 

                           coef exp(coef)  se(coef)      z Pr(>|z|)    
YEAR_OF_DIAGNOSIS2005 -0.053118  0.948268  0.018795 -2.826 0.004711 ** 
YEAR_OF_DIAGNOSIS2006  0.021053  1.021276  0.018853  1.117 0.264123    
YEAR_OF_DIAGNOSIS2007  0.002556  1.002560  0.019105  0.134 0.893555    
YEAR_OF_DIAGNOSIS2008  0.069987  1.072494  0.019028  3.678 0.000235 ***
YEAR_OF_DIAGNOSIS2009  0.036219  1.036883  0.019392  1.868 0.061795 .  
YEAR_OF_DIAGNOSIS2010  0.049316  1.050552  0.019750  2.497 0.012523 *  
YEAR_OF_DIAGNOSIS2011  0.074201  1.077023  0.019964  3.717 0.000202 ***
YEAR_OF_DIAGNOSIS2012  0.013694  1.013788  0.020779  0.659 0.509882    
YEAR_OF_DIAGNOSIS2013  0.030229  1.030691  0.021360  1.415 0.157002    
YEAR_OF_DIAGNOSIS2014 -0.003917  0.996091  0.022838 -0.171 0.863837    
YEAR_OF_DIAGNOSIS2015  0.006329  1.006349  0.024957  0.254 0.799809    
YEAR_OF_DIAGNOSIS2016        NA        NA  0.000000     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                      exp(coef) exp(-coef) lower .95 upper .95
YEAR_OF_DIAGNOSIS2005    0.9483     1.0546    0.9140    0.9839
YEAR_OF_DIAGNOSIS2006    1.0213     0.9792    0.9842    1.0597
YEAR_OF_DIAGNOSIS2007    1.0026     0.9974    0.9657    1.0408
YEAR_OF_DIAGNOSIS2008    1.0725     0.9324    1.0332    1.1132
YEAR_OF_DIAGNOSIS2009    1.0369     0.9644    0.9982    1.0771
YEAR_OF_DIAGNOSIS2010    1.0506     0.9519    1.0107    1.0920
YEAR_OF_DIAGNOSIS2011    1.0770     0.9285    1.0357    1.1200
YEAR_OF_DIAGNOSIS2012    1.0138     0.9864    0.9733    1.0559
YEAR_OF_DIAGNOSIS2013    1.0307     0.9702    0.9884    1.0748
YEAR_OF_DIAGNOSIS2014    0.9961     1.0039    0.9525    1.0417
YEAR_OF_DIAGNOSIS2015    1.0063     0.9937    0.9583    1.0568
YEAR_OF_DIAGNOSIS2016        NA         NA        NA        NA

Concordance= 0.507  (se = 0.001 )
Rsquare= 0   (max possible= 0.994 )
Likelihood ratio test= 69.6  on 11 df,   p=1.453e-10
Wald test            = 69.29  on 11 df,   p=1.667e-10
Score (logrank) test = 69.33  on 11 df,   p=1.64e-10
Removed 2 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  YEAR_OF_DIAGNOSIS
This manual palette can handle a maximum of 10 values. You have supplied 12.

Primary Site

uni_var(test_var = "SITE_TEXT", data_imp = data)
_________________________________________________
   
## SITE_TEXT
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SITE_TEXT, data = data)

                                                              n events median 0.95LCL 0.95UCL
SITE_TEXT=C00.0 External Lip: Upper NOS                       6      1     NA      NA      NA
SITE_TEXT=C00.1 External Lip: Lower NOS                       8      2     NA    56.2      NA
SITE_TEXT=C00.2  External Lip: NOS                            1      0     NA      NA      NA
SITE_TEXT=C00.3 Lip: Upper Mucosa                             3      2   15.8    10.6      NA
SITE_TEXT=C00.4 Lip: Lower Mucosa                             6      3   34.0    25.4      NA
SITE_TEXT=C00.5 Lip: Mucosa NOS                               2      0     NA      NA      NA
SITE_TEXT=C00.6 Lip: Commissure                               1      0     NA      NA      NA
SITE_TEXT=C00.9 Lip, NOS                                      1      1   85.5      NA      NA
SITE_TEXT=C44.0 Skin of lip, NOS                            451    111  155.0   139.4      NA
SITE_TEXT=C44.1 Eyelid                                      768    205     NA      NA      NA
SITE_TEXT=C44.2 External ear                               7522   1857  155.0   143.4      NA
SITE_TEXT=C44.3 Skin of ear and unspecified parts of face 22996   6261  138.9   134.1   144.5
SITE_TEXT=C44.4 Skin of scalp and neck                    20868   6011  146.2   139.1   161.5
SITE_TEXT=C44.5 Skin of trunk                             84369  14949     NA      NA      NA
SITE_TEXT=C44.6 Skin of upper limb and shoulder           65905  11730     NA   164.6      NA
SITE_TEXT=C44.7 Skin of lower limb and hip                52572   8181     NA      NA      NA
SITE_TEXT=C44.8 Overlapping lesion of skin                  288    102  125.5    87.0      NA
SITE_TEXT=C44.9 Skin, NOS                                 11444   7850   10.7    10.2    11.3
SITE_TEXT=C51.0 Labium majus                                127     57   57.3    39.4      NA
SITE_TEXT=C51.1 Labium minus                                 96     50   58.4    40.4   127.8
SITE_TEXT=C51.2 Clitoris                                     46     32   23.3    16.1    50.8
SITE_TEXT=C51.8 Overlapping lesion of vulva                  46     25   41.4    30.5      NA
SITE_TEXT=C51.9 Vulva, NOS                                 1121    593   45.1    39.3    54.3
SITE_TEXT=C52.9 Vagina, NOS                                 388    311   18.5    17.0    20.4
SITE_TEXT=C60.0 Prepuce                                       8      6   17.9    13.9      NA
SITE_TEXT=C60.1 Glans penis                                  13      6   79.5    33.4      NA
SITE_TEXT=C60.2 Body of penis                                 2      1   32.0    32.0      NA
SITE_TEXT=C60.8 Overlapping lesion of penis                   1      1   73.2      NA      NA
SITE_TEXT=C60.9 Penis                                        30     12     NA    40.0      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SITE_TEXT, data = data)

                SITE_TEXT=C00.0 External Lip: Upper NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       0    1.000   0.000        1.000            1
   24      5       1    0.833   0.152        0.583            1
   36      4       0    0.833   0.152        0.583            1
   48      3       0    0.833   0.152        0.583            1
   60      3       0    0.833   0.152        0.583            1

                SITE_TEXT=C00.1 External Lip: Lower NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      8       0    1.000   0.000        1.000            1
   24      7       1    0.875   0.117        0.673            1
   36      7       0    0.875   0.117        0.673            1
   48      6       0    0.875   0.117        0.673            1
   60      3       1    0.656   0.209        0.352            1

                SITE_TEXT=C00.2  External Lip: NOS 
     time n.risk n.event survival std.err lower 95% CI upper 95% CI

                SITE_TEXT=C00.3 Lip: Upper Mucosa 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      12.000        1.000        1.000        0.500        0.354        0.125        1.000 

                SITE_TEXT=C00.4 Lip: Lower Mucosa 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      5       1    0.833   0.152        0.583            1
   24      5       0    0.833   0.152        0.583            1
   36      1       2    0.444   0.222        0.167            1
   48      1       0    0.444   0.222        0.167            1

                SITE_TEXT=C00.5 Lip: Mucosa NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      2       0        1       0            1            1
   36      1       0        1       0            1            1
   48      1       0        1       0            1            1
   60      1       0        1       0            1            1

                SITE_TEXT=C00.6 Lip: Commissure 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
          12            1            0            1            0            1            1 

                SITE_TEXT=C00.9 Lip, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      1       0        1       0            1            1
   24      1       0        1       0            1            1
   36      1       0        1       0            1            1
   48      1       0        1       0            1            1
   60      1       0        1       0            1            1

                SITE_TEXT=C44.0 Skin of lip, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    384      18    0.958 0.00975        0.939        0.977
   24    339      21    0.903 0.01474        0.875        0.933
   36    283      20    0.847 0.01848        0.811        0.884
   48    222      13    0.804 0.02101        0.764        0.846
   60    179      12    0.757 0.02378        0.712        0.805
  120     39      24    0.595 0.03748        0.526        0.673

                SITE_TEXT=C44.1 Eyelid 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    658      39    0.946 0.00835        0.930        0.963
   24    559      41    0.885 0.01216        0.861        0.909
   36    460      31    0.832 0.01464        0.804        0.862
   48    387      27    0.781 0.01679        0.748        0.814
   60    314      20    0.737 0.01849        0.701        0.774
  120     74      46    0.566 0.02779        0.514        0.623

                SITE_TEXT=C44.2 External ear 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   6388     335    0.952 0.00254        0.947        0.957
   24   5443     358    0.896 0.00374        0.889        0.904
   36   4535     280    0.847 0.00454        0.838        0.856
   48   3703     261    0.795 0.00529        0.785        0.806
   60   3010     154    0.759 0.00579        0.748        0.771
  120    671     420    0.592 0.00907        0.575        0.610

                SITE_TEXT=C44.3 Skin of ear and unspecified parts of face 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  19618    1038    0.952 0.00146        0.949        0.955
   24  16652    1257    0.888 0.00221        0.884        0.892
   36  13604    1007    0.831 0.00271        0.825        0.836
   48  11087     760    0.781 0.00309        0.775        0.787
   60   8834     632    0.733 0.00345        0.726        0.740
  120   1928    1398    0.550 0.00531        0.539        0.560

                SITE_TEXT=C44.4 Skin of scalp and neck 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  17584    1258    0.936 0.00175        0.933        0.939
   24  14643    1354    0.861 0.00254        0.856        0.866
   36  11674    1078    0.793 0.00307        0.787        0.799
   48   9306     717    0.740 0.00344        0.733        0.747
   60   7379     481    0.698 0.00374        0.691        0.706
  120   1591    1034    0.543 0.00548        0.533        0.554

                SITE_TEXT=C44.5 Skin of trunk 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  71804    3073    0.961 0.000689        0.960        0.962
   24  62422    3079    0.918 0.001004        0.916        0.920
   36  52198    2447    0.880 0.001227        0.877        0.882
   48  43541    1822    0.847 0.001403        0.844        0.849
   60  35620    1219    0.821 0.001542        0.818        0.824
  120   8995    2932    0.714 0.002393        0.710        0.719

                SITE_TEXT=C44.6 Skin of upper limb and shoulder 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  56735    1929    0.969 0.00070        0.967        0.970
   24  49405    2356    0.927 0.00108        0.925        0.929
   36  41368    1884    0.889 0.00134        0.886        0.892
   48  34326    1392    0.857 0.00155        0.854        0.860
   60  27935    1112    0.827 0.00174        0.823        0.830
  120   6747    2689    0.699 0.00286        0.693        0.704

                SITE_TEXT=C44.7 Skin of lower limb and hip 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  45292    1556    0.968 0.000787        0.967        0.970
   24  39623    1714    0.930 0.001180        0.928        0.933
   36  33349    1441    0.894 0.001467        0.891        0.897
   48  28111     953    0.867 0.001669        0.864        0.870
   60  23016     768    0.841 0.001860        0.838        0.845
  120   6000    1537    0.753 0.002833        0.747        0.758

                SITE_TEXT=C44.8 Overlapping lesion of skin 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    223      42    0.847  0.0218        0.805        0.891
   24    187      19    0.772  0.0257        0.724        0.824
   36    157      14    0.712  0.0283        0.659        0.770
   48    121       6    0.683  0.0295        0.628        0.744
   60    101       5    0.653  0.0312        0.594        0.717
  120     24      14    0.518  0.0422        0.441        0.608

                SITE_TEXT=C44.9 Skin, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4946    5732    0.480 0.00479        0.471        0.489
   24   3382    1127    0.366 0.00471        0.357        0.375
   36   2449     477    0.310 0.00463        0.301        0.320
   48   1888     201    0.283 0.00461        0.274        0.292
   60   1463     119    0.263 0.00463        0.255        0.273
  120    332     172    0.215 0.00522        0.205        0.226

                SITE_TEXT=C51.0 Labium majus 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     98      15    0.874  0.0306        0.816        0.936
   24     74      15    0.736  0.0416        0.659        0.822
   36     56      11    0.621  0.0475        0.534        0.721
   48     44       6    0.551  0.0500        0.461        0.658
   60     34       6    0.472  0.0521        0.381        0.587
  120      3       4    0.388  0.0611        0.285        0.528

                SITE_TEXT=C51.1 Labium minus 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     79      14    0.852  0.0366        0.783        0.927
   24     63      12    0.717  0.0471        0.630        0.816
   36     47       9    0.605  0.0526        0.510        0.717
   48     35       6    0.520  0.0555        0.422        0.641
   60     28       2    0.489  0.0564        0.390        0.613
  120      5       6    0.329  0.0705        0.217        0.501

                SITE_TEXT=C51.2 Clitoris 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     30      14    0.686  0.0696        0.563        0.837
   24     20       9    0.477  0.0758        0.349        0.651
   36     15       4    0.377  0.0745        0.256        0.556
   48     10       1    0.350  0.0739        0.232        0.530
   60      5       2    0.263  0.0771        0.148        0.467

                SITE_TEXT=C51.8 Overlapping lesion of vulva 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     40       5    0.891  0.0459        0.806        0.986
   24     33       4    0.799  0.0600        0.690        0.926
   36     16      11    0.503  0.0808        0.367        0.689
   48     13       1    0.470  0.0821        0.333        0.662
   60     10       3    0.361  0.0837        0.229        0.569
  120      3       0    0.361  0.0837        0.229        0.569

                SITE_TEXT=C51.9 Vulva, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    884     175    0.839  0.0112        0.817        0.861
   24    637     170    0.670  0.0146        0.642        0.699
   36    472     103    0.555  0.0159        0.525        0.587
   48    353      58    0.482  0.0165        0.451        0.516
   60    266      30    0.437  0.0169        0.405        0.471
  120     56      55    0.301  0.0204        0.263        0.344

                SITE_TEXT=C52.9 Vagina, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    246     130   0.6571  0.0244       0.6109        0.707
   24    138     100   0.3841  0.0253       0.3376        0.437
   36     74      46   0.2492  0.0230       0.2079        0.299
   48     49      17   0.1890  0.0216       0.1510        0.237
   60     33       6   0.1633  0.0211       0.1268        0.210
  120      6      11   0.0906  0.0208       0.0578        0.142

                SITE_TEXT=C60.0 Prepuce 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       2    0.750   0.153       0.5027        1.000
   24      3       3    0.375   0.171       0.1533        0.917
   36      3       0    0.375   0.171       0.1533        0.917
   48      2       1    0.250   0.153       0.0753        0.830
   60      1       0    0.250   0.153       0.0753        0.830
  120      1       0    0.250   0.153       0.0753        0.830

                SITE_TEXT=C60.1 Glans penis 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     13       0    1.000   0.000        1.000        1.000
   24      8       3    0.750   0.125        0.541        1.000
   36      5       1    0.656   0.140        0.432        0.997
   48      4       1    0.525   0.162        0.286        0.962
   60      4       0    0.525   0.162        0.286        0.962
  120      1       1    0.350   0.179        0.128        0.955

                SITE_TEXT=C60.2 Body of penis 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0      1.0   0.000        1.000            1
   24      2       0      1.0   0.000        1.000            1
   36      1       1      0.5   0.354        0.125            1
   48      1       0      0.5   0.354        0.125            1
   60      1       0      0.5   0.354        0.125            1

                SITE_TEXT=C60.8 Overlapping lesion of penis 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      1       0        1       0            1            1
   24      1       0        1       0            1            1
   36      1       0        1       0            1            1
   48      1       0        1       0            1            1
   60      1       0        1       0            1            1

                SITE_TEXT=C60.9 Penis 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     27       1    0.966  0.0339        0.901        1.000
   24     22       5    0.787  0.0773        0.649        0.954
   36     17       3    0.674  0.0897        0.519        0.875
   48     12       1    0.634  0.0927        0.476        0.845
   60      8       2    0.517  0.1070        0.344        0.775
  120      1       0    0.517  0.1070        0.344        0.775




   
## Univariable Cox Proportional Hazard Model for:  SITE_TEXT
Loglik converged before variable  2,5,6 ; beta may be infinite. X matrix deemed to be singular; variable 7 19
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SITE_TEXT, data = data)

  n= 269089, number of events= 58360 

                                                               coef  exp(coef)   se(coef)      z Pr(>|z|)  
SITE_TEXTC00.1 External Lip: Lower NOS                    4.050e-01  1.499e+00  1.225e+00  0.331   0.7409  
SITE_TEXTC00.2  External Lip: NOS                        -7.597e+00  5.018e-04  3.025e+02 -0.025   0.9800  
SITE_TEXTC00.3 Lip: Upper Mucosa                          2.616e+00  1.368e+01  1.225e+00  2.136   0.0327 *
SITE_TEXTC00.4 Lip: Lower Mucosa                          1.633e+00  5.117e+00  1.155e+00  1.414   0.1574  
SITE_TEXTC00.5 Lip: Mucosa NOS                           -7.528e+00  5.379e-04  7.366e+01 -0.102   0.9186  
SITE_TEXTC00.6 Lip: Commissure                           -7.566e+00  5.179e-04  1.787e+02 -0.042   0.9662  
SITE_TEXTC00.8 Lip: Overlapping                                  NA         NA  0.000e+00     NA       NA  
SITE_TEXTC00.9 Lip, NOS                                   1.438e+00  4.210e+00  1.414e+00  1.017   0.3094  
SITE_TEXTC44.0 Skin of lip, NOS                           4.267e-01  1.532e+00  1.004e+00  0.425   0.6710  
SITE_TEXTC44.1 Eyelid                                     5.092e-01  1.664e+00  1.002e+00  0.508   0.6115  
SITE_TEXTC44.2 External ear                               4.429e-01  1.557e+00  1.000e+00  0.443   0.6579  
SITE_TEXTC44.3 Skin of ear and unspecified parts of face  5.575e-01  1.746e+00  1.000e+00  0.557   0.5772  
SITE_TEXTC44.4 Skin of scalp and neck                     6.551e-01  1.925e+00  1.000e+00  0.655   0.5124  
SITE_TEXTC44.5 Skin of trunk                              7.515e-02  1.078e+00  1.000e+00  0.075   0.9401  
SITE_TEXTC44.6 Skin of upper limb and shoulder            7.490e-02  1.078e+00  1.000e+00  0.075   0.9403  
SITE_TEXTC44.7 Skin of lower limb and hip                -8.118e-02  9.220e-01  1.000e+00 -0.081   0.9353  
SITE_TEXTC44.8 Overlapping lesion of skin                 9.134e-01  2.493e+00  1.005e+00  0.909   0.3634  
SITE_TEXTC44.9 Skin, NOS                                  2.265e+00  9.633e+00  1.000e+00  2.265   0.0235 *
SITE_TEXTC50.0 Nipple                                            NA         NA  0.000e+00     NA       NA  
SITE_TEXTC51.0 Labium majus                               1.289e+00  3.628e+00  1.009e+00  1.278   0.2014  
SITE_TEXTC51.1 Labium minus                               1.339e+00  3.817e+00  1.010e+00  1.326   0.1848  
SITE_TEXTC51.2 Clitoris                                   2.063e+00  7.866e+00  1.016e+00  2.031   0.0423 *
SITE_TEXTC51.8 Overlapping lesion of vulva                1.451e+00  4.269e+00  1.020e+00  1.423   0.1547  
SITE_TEXTC51.9 Vulva, NOS                                 1.470e+00  4.348e+00  1.001e+00  1.468   0.1420  
SITE_TEXTC52.9 Vagina, NOS                                2.321e+00  1.018e+01  1.002e+00  2.317   0.0205 *
SITE_TEXTC60.0 Prepuce                                    1.877e+00  6.532e+00  1.080e+00  1.737   0.0823 .
SITE_TEXTC60.1 Glans penis                                1.165e+00  3.204e+00  1.080e+00  1.078   0.2810  
SITE_TEXTC60.2 Body of penis                              1.087e+00  2.966e+00  1.414e+00  0.769   0.4420  
SITE_TEXTC60.8 Overlapping lesion of penis                1.556e+00  4.742e+00  1.414e+00  1.101   0.2711  
SITE_TEXTC60.9 Penis                                      1.047e+00  2.849e+00  1.041e+00  1.006   0.3145  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                         exp(coef) exp(-coef)  lower .95  upper .95
SITE_TEXTC00.1 External Lip: Lower NOS                   1.499e+00  6.670e-01  1.360e-01  1.654e+01
SITE_TEXTC00.2  External Lip: NOS                        5.018e-04  1.993e+03 1.546e-261 1.628e+254
SITE_TEXTC00.3 Lip: Upper Mucosa                         1.368e+01  7.311e-02  1.240e+00  1.509e+02
SITE_TEXTC00.4 Lip: Lower Mucosa                         5.117e+00  1.954e-01  5.323e-01  4.919e+01
SITE_TEXTC00.5 Lip: Mucosa NOS                           5.379e-04  1.859e+03  1.069e-66  2.706e+59
SITE_TEXTC00.6 Lip: Commissure                           5.179e-04  1.931e+03 4.185e-156 6.409e+148
SITE_TEXTC00.8 Lip: Overlapping                                 NA         NA         NA         NA
SITE_TEXTC00.9 Lip, NOS                                  4.210e+00  2.375e-01  2.634e-01  6.731e+01
SITE_TEXTC44.0 Skin of lip, NOS                          1.532e+00  6.527e-01  2.139e-01  1.097e+01
SITE_TEXTC44.1 Eyelid                                    1.664e+00  6.010e-01  2.333e-01  1.187e+01
SITE_TEXTC44.2 External ear                              1.557e+00  6.422e-01  2.192e-01  1.106e+01
SITE_TEXTC44.3 Skin of ear and unspecified parts of face 1.746e+00  5.726e-01  2.459e-01  1.240e+01
SITE_TEXTC44.4 Skin of scalp and neck                    1.925e+00  5.194e-01  2.712e-01  1.367e+01
SITE_TEXTC44.5 Skin of trunk                             1.078e+00  9.276e-01  1.518e-01  7.654e+00
SITE_TEXTC44.6 Skin of upper limb and shoulder           1.078e+00  9.278e-01  1.518e-01  7.652e+00
SITE_TEXTC44.7 Skin of lower limb and hip                9.220e-01  1.085e+00  1.299e-01  6.546e+00
SITE_TEXTC44.8 Overlapping lesion of skin                2.493e+00  4.012e-01  3.478e-01  1.787e+01
SITE_TEXTC44.9 Skin, NOS                                 9.633e+00  1.038e-01  1.357e+00  6.840e+01
SITE_TEXTC50.0 Nipple                                           NA         NA         NA         NA
SITE_TEXTC51.0 Labium majus                              3.628e+00  2.756e-01  5.024e-01  2.620e+01
SITE_TEXTC51.1 Labium minus                              3.817e+00  2.620e-01  5.273e-01  2.763e+01
SITE_TEXTC51.2 Clitoris                                  7.866e+00  1.271e-01  1.075e+00  5.756e+01
SITE_TEXTC51.8 Overlapping lesion of vulva               4.269e+00  2.343e-01  5.784e-01  3.150e+01
SITE_TEXTC51.9 Vulva, NOS                                4.348e+00  2.300e-01  6.114e-01  3.092e+01
SITE_TEXTC52.9 Vagina, NOS                               1.018e+01  9.819e-02  1.430e+00  7.253e+01
SITE_TEXTC60.0 Prepuce                                   6.532e+00  1.531e-01  7.864e-01  5.425e+01
SITE_TEXTC60.1 Glans penis                               3.204e+00  3.121e-01  3.858e-01  2.662e+01
SITE_TEXTC60.2 Body of penis                             2.966e+00  3.371e-01  1.855e-01  4.742e+01
SITE_TEXTC60.8 Overlapping lesion of penis               4.742e+00  2.109e-01  2.966e-01  7.581e+01
SITE_TEXTC60.9 Penis                                     2.849e+00  3.510e-01  3.705e-01  2.191e+01

Concordance= 0.635  (se = 0.001 )
Rsquare= 0.081   (max possible= 0.994 )
Likelihood ratio test= 22616  on 28 df,   p=0
Wald test            = 33458  on 28 df,   p=0
Score (logrank) test = 46775  on 28 df,   p=0
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 3 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SITE_TEXT
This manual palette can handle a maximum of 10 values. You have supplied 29.

Histology

uni_var(test_var = "HISTOLOGY_F_LIM", data_imp = data)
_________________________________________________
   
## HISTOLOGY_F_LIM
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISTOLOGY_F_LIM, data = data)

                           n events median 0.95LCL 0.95UCL
HISTOLOGY_F_LIM=8720  136954  33081     NA      NA      NA
HISTOLOGY_F_LIM=Other 132135  25279     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISTOLOGY_F_LIM, data = data)

                HISTOLOGY_F_LIM=8720 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 112054   10919    0.916 0.000769        0.915        0.918
   24  96719    6183    0.864 0.000974        0.862        0.865
   36  81131    4512    0.821 0.001115        0.819        0.823
   48  67828    3122    0.787 0.001222        0.785        0.789
   60  55654    2307    0.758 0.001318        0.756        0.761
  120  13991    5353    0.643 0.001919        0.639        0.647

                HISTOLOGY_F_LIM=Other 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 113028    4459    0.964 0.000532        0.963        0.965
   24  96957    5467    0.915 0.000818        0.913        0.917
   36  79667    4358    0.871 0.001016        0.869        0.873
   48  65400    3122    0.834 0.001168        0.832        0.836
   60  52596    2267    0.803 0.001299        0.800        0.805
  120  12486    4994    0.681 0.002035        0.677        0.685




   
## Univariable Cox Proportional Hazard Model for:  HISTOLOGY_F_LIM
X matrix deemed to be singular; variable 2 3
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISTOLOGY_F_LIM, data = data)

  n= 269089, number of events= 58360 

                         coef exp(coef) se(coef)     z Pr(>|z|)    
HISTOLOGY_F_LIM8720  0.244112  1.276487 0.008355 29.22   <2e-16 ***
HISTOLOGY_F_LIM8520        NA        NA 0.000000    NA       NA    
HISTOLOGY_F_LIMOther       NA        NA 0.000000    NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                     exp(coef) exp(-coef) lower .95 upper .95
HISTOLOGY_F_LIM8720      1.276     0.7834     1.256     1.298
HISTOLOGY_F_LIM8520         NA         NA        NA        NA
HISTOLOGY_F_LIMOther        NA         NA        NA        NA

Concordance= 0.544  (se = 0.001 )
Rsquare= 0.003   (max possible= 0.994 )
Likelihood ratio test= 860.8  on 1 df,   p=0
Wald test            = 853.6  on 1 df,   p=0
Score (logrank) test = 857.8  on 1 df,   p=0
Removed 3 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  HISTOLOGY_F_LIM

Grade

uni_var(test_var = "GRADE_F", data_imp = data)
_________________________________________________
   
## GRADE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ GRADE_F, data = data)

                                      n events median 0.95LCL 0.95UCL
GRADE_F=Gr I: Well Diff             589    125     NA      NA      NA
GRADE_F=Gr II: Mod Diff             816    157     NA      NA      NA
GRADE_F=Gr III: Poor Diff          1497    761     47    39.1    59.6
GRADE_F=Gr IV: Undiff/Anaplastic    521    217     82    59.9   114.8
GRADE_F=NA/Unkown                265666  57100     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ GRADE_F, data = data)

                GRADE_F=Gr I: Well Diff 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    495      26    0.953  0.0091        0.935        0.971
   24    423      28    0.896  0.0134        0.870        0.923
   36    342      20    0.849  0.0163        0.818        0.882
   48    267      12    0.816  0.0183        0.781        0.853
   60    228       8    0.790  0.0199        0.752        0.830
  120     61      30    0.651  0.0291        0.597        0.711

                GRADE_F=Gr II: Mod Diff 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    708      29    0.962 0.00697        0.948        0.976
   24    620      41    0.904 0.01094        0.883        0.926
   36    528      27    0.862 0.01308        0.837        0.888
   48    459      11    0.843 0.01395        0.817        0.871
   60    392      10    0.824 0.01492        0.795        0.854
  120    115      37    0.716 0.02165        0.675        0.760

                GRADE_F=Gr III: Poor Diff 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1004     380    0.735  0.0117        0.713        0.758
   24    746     170    0.606  0.0132        0.580        0.632
   36    592      74    0.542  0.0137        0.516        0.569
   48    483      47    0.497  0.0141        0.470        0.525
   60    386      23    0.471  0.0144        0.444        0.500
  120     90      58    0.366  0.0171        0.333        0.401

                GRADE_F=Gr IV: Undiff/Anaplastic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    384      86    0.825  0.0172        0.792        0.859
   24    294      49    0.713  0.0210        0.673        0.756
   36    227      31    0.632  0.0232        0.588        0.679
   48    185      17    0.582  0.0243        0.537        0.632
   60    153      11    0.546  0.0252        0.498        0.597
  120     37      22    0.416  0.0325        0.357        0.485

                GRADE_F=NA/Unkown 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 222491   14857    0.941 0.000471        0.940        0.942
   24 191593   11362    0.891 0.000639        0.889        0.892
   36 159109    8718    0.847 0.000758        0.846        0.849
   48 131834    6157    0.812 0.000850        0.811        0.814
   60 107091    4522    0.782 0.000930        0.780        0.784
  120  26174   10200    0.664 0.001406        0.661        0.667




   
## Univariable Cox Proportional Hazard Model for:  GRADE_F
X matrix deemed to be singular; variable 4 5 6 7
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ GRADE_F, data = data)

  n= 269089, number of events= 58360 

                                     coef exp(coef)  se(coef)      z Pr(>|z|)    
GRADE_FGr II: Mod Diff          -0.214649  0.806825  0.119874 -1.791   0.0734 .  
GRADE_FGr III: Poor Diff         1.189850  3.286589  0.096512 12.329  < 2e-16 ***
GRADE_FGr IV: Undiff/Anaplastic  0.888026  2.430326  0.112288  7.908 2.55e-15 ***
GRADE_F5                               NA        NA  0.000000     NA       NA    
GRADE_F6                               NA        NA  0.000000     NA       NA    
GRADE_F7                               NA        NA  0.000000     NA       NA    
GRADE_F8                               NA        NA  0.000000     NA       NA    
GRADE_FNA/Unkown                 0.006778  1.006801  0.089541  0.076   0.9397    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                exp(coef) exp(-coef) lower .95 upper .95
GRADE_FGr II: Mod Diff             0.8068     1.2394    0.6379     1.021
GRADE_FGr III: Poor Diff           3.2866     0.3043    2.7202     3.971
GRADE_FGr IV: Undiff/Anaplastic    2.4303     0.4115    1.9502     3.029
GRADE_F5                               NA         NA        NA        NA
GRADE_F6                               NA         NA        NA        NA
GRADE_F7                               NA         NA        NA        NA
GRADE_F8                               NA         NA        NA        NA
GRADE_FNA/Unkown                   1.0068     0.9932    0.8447     1.200

Concordance= 0.507  (se = 0 )
Rsquare= 0.003   (max possible= 0.994 )
Likelihood ratio test= 874.7  on 4 df,   p=0
Wald test            = 1222  on 4 df,   p=0
Score (logrank) test = 1361  on 4 df,   p=0
Removed 5 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  GRADE_F

Clinical T Stage

uni_var(test_var = "TNM_CLIN_T", data_imp = data)
_________________________________________________
   
## TNM_CLIN_T
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_T, data = data)

   7214 observations deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
TNM_CLIN_T=N_A   1574    679  131.0   115.9      NA
TNM_CLIN_T=c0    3237   2060   13.4    12.1    15.1
TNM_CLIN_T=c1   14071   1652     NA      NA      NA
TNM_CLIN_T=c1A  39363   2098   95.5    95.5      NA
TNM_CLIN_T=c1B  15597   1119   97.1      NA      NA
TNM_CLIN_T=c2    4134    864     NA      NA      NA
TNM_CLIN_T=c2A  25371   3496     NA      NA      NA
TNM_CLIN_T=c2B   6395   1587  141.2   124.0      NA
TNM_CLIN_T=c3    2398    859  100.5    92.8   113.3
TNM_CLIN_T=c3A   9810   2508  133.8   126.0   151.1
TNM_CLIN_T=c3B   7776   2953   77.7    72.9    81.9
TNM_CLIN_T=c4    1674    900   42.2    37.7    47.4
TNM_CLIN_T=c4A   4516   1746   80.1    75.4    86.8
TNM_CLIN_T=c4B   8256   4723   35.5    34.1    37.0
TNM_CLIN_T=cX  115943  29551     NA      NA      NA
TNM_CLIN_T=pIS   1760    263     NA   157.9      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_T, data = data)

7214 observations deleted due to missingness 
                TNM_CLIN_T=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1280     213    0.861 0.00885        0.844        0.878
   24   1070     171    0.744 0.01131        0.722        0.766
   36    935      93    0.678 0.01219        0.655        0.702
   48    837      61    0.633 0.01268        0.608        0.658
   60    761      30    0.610 0.01291        0.585        0.635
  120    298      94    0.520 0.01407        0.493        0.548

                TNM_CLIN_T=c0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1510    1501    0.518 0.00900        0.501        0.536
   24   1037     316    0.404 0.00903        0.387        0.423
   36    744     123    0.352 0.00902        0.335        0.370
   48    555      54    0.324 0.00907        0.307        0.342
   60    408      25    0.308 0.00920        0.290        0.326
  120     72      34    0.262 0.01126        0.241        0.285

                TNM_CLIN_T=c1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  12185     242    0.982 0.00118        0.979        0.984
   24  11045     257    0.960 0.00176        0.957        0.963
   36   9697     251    0.937 0.00224        0.933        0.941
   48   8219     207    0.916 0.00264        0.910        0.921
   60   6686     177    0.894 0.00303        0.888        0.900
  120   1163     465    0.777 0.00634        0.765        0.789

                TNM_CLIN_T=c1A 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  32652     391    0.989 0.000554        0.988        0.990
   24  27240     426    0.975 0.000866        0.973        0.977
   36  20452     416    0.958 0.001185        0.956        0.960
   48  14346     371    0.938 0.001570        0.935        0.941
   60   8893     236    0.918 0.001983        0.915        0.922

                TNM_CLIN_T=c1B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  12927     214    0.985 0.00103        0.983        0.987
   24  10511     294    0.961 0.00173        0.957        0.964
   36   7455     238    0.935 0.00234        0.931        0.940
   48   4977     177    0.909 0.00302        0.903        0.915
   60   2901      94    0.887 0.00371        0.879        0.894

                TNM_CLIN_T=c2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3554     139    0.964 0.00300        0.958        0.970
   24   3137     166    0.917 0.00455        0.908        0.926
   36   2687     140    0.874 0.00560        0.863        0.885
   48   2288     105    0.838 0.00639        0.826        0.851
   60   1813     109    0.795 0.00729        0.781        0.809
  120    241     189    0.640 0.01345        0.614        0.666

                TNM_CLIN_T=c2A 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  21935     429    0.982 0.000871        0.980        0.984
   24  18828     661    0.950 0.001469        0.948        0.953
   36  15148     592    0.918 0.001938        0.914        0.922
   48  12052     512    0.884 0.002381        0.879        0.888
   60   9286     409    0.850 0.002814        0.845        0.856
  120   1401     817    0.708 0.005778        0.696        0.719

                TNM_CLIN_T=c2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   5451     271    0.955 0.00269        0.949        0.960
   24   4504     364    0.888 0.00422        0.879        0.896
   36   3507     309    0.821 0.00534        0.811        0.832
   48   2654     221    0.764 0.00621        0.752        0.776
   60   1947     159    0.712 0.00701        0.699        0.726
  120    245     245    0.542 0.01246        0.518        0.567

                TNM_CLIN_T=c3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2030     177    0.922 0.00561        0.911        0.933
   24   1735     181    0.838 0.00787        0.823        0.853
   36   1390     175    0.749 0.00950        0.730        0.767
   48   1078     121    0.679 0.01052        0.658        0.700
   60    810      85    0.621 0.01137        0.599        0.643
  120     91     113    0.460 0.01789        0.426        0.497

                TNM_CLIN_T=c3A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   8452     348    0.962 0.00199        0.958        0.966
   24   7078     561    0.895 0.00329        0.889        0.902
   36   5449     526    0.823 0.00430        0.814        0.831
   48   4240     356    0.764 0.00500        0.754        0.774
   60   3188     245    0.715 0.00557        0.704        0.726
  120    450     437    0.537 0.00972        0.519        0.557

                TNM_CLIN_T=c3B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   6573     535    0.928 0.00302        0.922        0.933
   24   5134     808    0.808 0.00474        0.799        0.817
   36   3708     642    0.698 0.00575        0.687        0.709
   48   2704     355    0.625 0.00634        0.612        0.637
   60   1938     221    0.568 0.00683        0.554        0.581
  120    237     365    0.380 0.01055        0.359        0.401

                TNM_CLIN_T=c4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1224     334    0.792  0.0101        0.773        0.812
   24    900     238    0.633  0.0123        0.609        0.657
   36    683     127    0.539  0.0130        0.515        0.565
   48    513      82    0.470  0.0134        0.445        0.497
   60    395      48    0.423  0.0137        0.397        0.450
  120     48      70    0.304  0.0170        0.272        0.339

                TNM_CLIN_T=c4A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3774     380    0.912 0.00431        0.904        0.921
   24   2932     477    0.791 0.00638        0.779        0.804
   36   2226     316    0.699 0.00744        0.685        0.714
   48   1684     220    0.625 0.00819        0.609        0.641
   60   1232     129    0.571 0.00875        0.554        0.589
  120    180     212    0.405 0.01253        0.381        0.430

                TNM_CLIN_T=c4B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   6226    1537    0.807 0.00442        0.799        0.816
   24   4289    1404    0.617 0.00559        0.606        0.628
   36   2906     785    0.495 0.00595        0.484        0.507
   48   1982     451    0.411 0.00613        0.399        0.423
   60   1364     224    0.359 0.00627        0.347        0.372
  120    137     313    0.220 0.00837        0.204        0.237

                TNM_CLIN_T=cX 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  98039    8110    0.927 0.000780        0.926        0.929
   24  88553    4914    0.879 0.000993        0.878        0.881
   36  79759    3927    0.839 0.001136        0.837        0.842
   48  72147    2773    0.809 0.001231        0.807        0.812
   60  64679    2288    0.783 0.001310        0.780        0.785
  120  21806    6523    0.676 0.001710        0.673        0.680

                TNM_CLIN_T=pIS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1515      37    0.977 0.00366        0.970        0.985
   24   1284      50    0.943 0.00593        0.932        0.955
   36   1043      45    0.907 0.00776        0.892        0.923
   48    808      37    0.872 0.00941        0.854        0.890
   60    619      26    0.840 0.01094        0.819        0.862
  120    108      65    0.679 0.02209        0.637        0.724




   
## Univariable Cox Proportional Hazard Model for:  TNM_CLIN_T
X matrix deemed to be singular; variable 5 6 10 17 18 20
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_T, data = data)

  n= 261875, number of events= 57058 
   (7214 observations deleted due to missingness)

                   coef exp(coef) se(coef)       z Pr(>|z|)    
TNM_CLIN_Tc0    1.25345   3.50242  0.04431  28.286  < 2e-16 ***
TNM_CLIN_Tc1   -1.26284   0.28285  0.04560 -27.693  < 2e-16 ***
TNM_CLIN_Tc1A  -1.72766   0.17770  0.04424 -39.056  < 2e-16 ***
TNM_CLIN_Tc1B  -1.38231   0.25100  0.04873 -28.367  < 2e-16 ***
TNM_CLIN_Tc1C        NA        NA  0.00000      NA       NA    
TNM_CLIN_Tc1MI       NA        NA  0.00000      NA       NA    
TNM_CLIN_Tc2   -0.63379   0.53058  0.05130 -12.353  < 2e-16 ***
TNM_CLIN_Tc2A  -0.98645   0.37290  0.04197 -23.505  < 2e-16 ***
TNM_CLIN_Tc2B  -0.32012   0.72606  0.04589  -6.975 3.06e-12 ***
TNM_CLIN_Tc2C        NA        NA  0.00000      NA       NA    
TNM_CLIN_Tc3    0.01776   1.01792  0.05138   0.346     0.73    
TNM_CLIN_Tc3A  -0.31928   0.72667  0.04330  -7.375 1.65e-13 ***
TNM_CLIN_Tc3B   0.18744   1.20616  0.04261   4.399 1.09e-05 ***
TNM_CLIN_Tc4    0.64894   1.91350  0.05087  12.757  < 2e-16 ***
TNM_CLIN_Tc4A   0.18253   1.20024  0.04527   4.032 5.52e-05 ***
TNM_CLIN_Tc4B   0.80048   2.22661  0.04112  19.468  < 2e-16 ***
TNM_CLIN_Tc4C        NA        NA  0.00000      NA       NA    
TNM_CLIN_Tc4D        NA        NA  0.00000      NA       NA    
TNM_CLIN_TcX   -0.60555   0.54577  0.03882 -15.600  < 2e-16 ***
TNM_CLIN_TpA         NA        NA  0.00000      NA       NA    
TNM_CLIN_TpIS  -0.89767   0.40752  0.07265 -12.357  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

               exp(coef) exp(-coef) lower .95 upper .95
TNM_CLIN_Tc0      3.5024     0.2855    3.2111    3.8202
TNM_CLIN_Tc1      0.2828     3.5354    0.2587    0.3093
TNM_CLIN_Tc1A     0.1777     5.6275    0.1629    0.1938
TNM_CLIN_Tc1B     0.2510     3.9841    0.2281    0.2762
TNM_CLIN_Tc1C         NA         NA        NA        NA
TNM_CLIN_Tc1MI        NA         NA        NA        NA
TNM_CLIN_Tc2      0.5306     1.8847    0.4798    0.5867
TNM_CLIN_Tc2A     0.3729     2.6817    0.3435    0.4049
TNM_CLIN_Tc2B     0.7261     1.3773    0.6636    0.7944
TNM_CLIN_Tc2C         NA         NA        NA        NA
TNM_CLIN_Tc3      1.0179     0.9824    0.9204    1.1258
TNM_CLIN_Tc3A     0.7267     1.3761    0.6676    0.7910
TNM_CLIN_Tc3B     1.2062     0.8291    1.1095    1.3112
TNM_CLIN_Tc4      1.9135     0.5226    1.7319    2.1141
TNM_CLIN_Tc4A     1.2002     0.8332    1.0983    1.3116
TNM_CLIN_Tc4B     2.2266     0.4491    2.0542    2.4135
TNM_CLIN_Tc4C         NA         NA        NA        NA
TNM_CLIN_Tc4D         NA         NA        NA        NA
TNM_CLIN_TcX      0.5458     1.8323    0.5058    0.5889
TNM_CLIN_TpA          NA         NA        NA        NA
TNM_CLIN_TpIS     0.4075     2.4539    0.3534    0.4699

Concordance= 0.66  (se = 0.001 )
Rsquare= 0.076   (max possible= 0.994 )
Likelihood ratio test= 20697  on 15 df,   p=0
Wald test            = 23632  on 15 df,   p=0
Score (logrank) test = 30076  on 15 df,   p=0
Removed 7 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_CLIN_T
This manual palette can handle a maximum of 10 values. You have supplied 16.

Clinical N Stage

uni_var(test_var = "TNM_CLIN_N", data_imp = data)
_________________________________________________
   
## TNM_CLIN_N
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_N, data = data)

   6157 observations deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
TNM_CLIN_N=N_A   1573    678  131.0   115.9      NA
TNM_CLIN_N=c0  180888  28469     NA      NA      NA
TNM_CLIN_N=c1    4432   2166   45.9    42.0    50.5
TNM_CLIN_N=c1A   1030    376  117.5   107.5      NA
TNM_CLIN_N=c1B   1267    740   26.6    24.1    29.7
TNM_CLIN_N=c2     905    477   37.0    31.9    44.8
TNM_CLIN_N=c2A    342    144  110.2    72.0      NA
TNM_CLIN_N=c2B    658    393   28.9    25.1    33.3
TNM_CLIN_N=c2C    630    330   42.0    36.8    50.1
TNM_CLIN_N=c3    2102   1497   15.5    14.3    16.7
TNM_CLIN_N=cX   69105  21865     NA   164.5      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_N, data = data)

6157 observations deleted due to missingness 
                TNM_CLIN_N=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1280     212    0.861 0.00884        0.844        0.879
   24   1070     171    0.744 0.01130        0.722        0.767
   36    935      93    0.678 0.01219        0.655        0.703
   48    837      61    0.633 0.01268        0.609        0.659
   60    761      30    0.610 0.01291        0.585        0.636
  120    298      94    0.520 0.01408        0.494        0.549

                TNM_CLIN_N=c0 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 153959    5295    0.969 0.000425        0.968        0.969
   24 132076    5910    0.929 0.000644        0.928        0.931
   36 107180    4986    0.891 0.000813        0.890        0.893
   48  85888    3619    0.859 0.000950        0.857        0.860
   60  66731    2684    0.829 0.001078        0.827        0.831
  120  11055    5473    0.705 0.001982        0.701        0.709

                TNM_CLIN_N=c1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3118     990    0.767 0.00649        0.755        0.780
   24   2294     521    0.633 0.00758        0.618        0.648
   36   1695     290    0.547 0.00806        0.532        0.563
   48   1268     159    0.492 0.00836        0.476        0.508
   60    927      88    0.453 0.00866        0.437        0.471
  120    108     108    0.358 0.01169        0.336        0.382

                TNM_CLIN_N=c1A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    909      64    0.936 0.00777        0.921        0.951
   24    756      96    0.834 0.01202        0.811        0.858
   36    627      75    0.748 0.01429        0.721        0.777
   48    511      57    0.677 0.01574        0.647        0.709
   60    407      30    0.633 0.01666        0.601        0.666
  120     79      50    0.499 0.02324        0.456        0.547

                TNM_CLIN_N=c1B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    829     353    0.711  0.0130        0.686        0.737
   24    573     204    0.531  0.0146        0.503        0.560
   36    414      89    0.443  0.0149        0.415        0.473
   48    317      46    0.390  0.0150        0.362        0.420
   60    251      12    0.374  0.0151        0.345        0.405
  120     48      31    0.300  0.0178        0.267        0.337

                TNM_CLIN_N=c2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    656     190    0.783  0.0140        0.756        0.811
   24    448     138    0.611  0.0170        0.578        0.645
   36    305      72    0.503  0.0181        0.469        0.540
   48    229      33    0.444  0.0187        0.409        0.482
   60    172      23    0.396  0.0192        0.361        0.436
  120     38      20    0.339  0.0204        0.301        0.381

                TNM_CLIN_N=c2A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    299      26    0.922  0.0146        0.894        0.951
   24    244      44    0.784  0.0230        0.740        0.830
   36    200      28    0.690  0.0262        0.640        0.743
   48    172      11    0.650  0.0272        0.599        0.706
   60    142      11    0.607  0.0284        0.554        0.665
  120     23      23    0.435  0.0421        0.360        0.526

                TNM_CLIN_N=c2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    467     166    0.743  0.0172        0.710        0.778
   24    314     113    0.555  0.0200        0.518        0.596
   36    228      57    0.450  0.0205        0.412        0.493
   48    176      28    0.392  0.0206        0.354        0.435
   60    133      11    0.365  0.0208        0.326        0.408
  120     20      18    0.276  0.0261        0.229        0.332

                TNM_CLIN_N=c2C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    480     109    0.821  0.0156        0.791        0.852
   24    345      90    0.660  0.0198        0.622        0.699
   36    244      54    0.549  0.0215        0.509        0.593
   48    164      34    0.465  0.0226        0.423        0.511
   60    112      19    0.402  0.0237        0.359        0.452
  120     16      24    0.290  0.0272        0.241        0.348

                TNM_CLIN_N=c3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1128     886    0.569  0.0110        0.547        0.590
   24    673     353    0.382  0.0110        0.361        0.404
   36    439     138    0.298  0.0107        0.277        0.319
   48    305      61    0.253  0.0105        0.233        0.274
   60    218      25    0.230  0.0105        0.210        0.251
  120     24      32    0.164  0.0143        0.138        0.195

                TNM_CLIN_N=cX 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  57081    6588    0.901 0.00116        0.899        0.903
   24  51040    3678    0.842 0.00144        0.839        0.844
   36  45763    2833    0.794 0.00161        0.790        0.797
   48  41332    2001    0.758 0.00173        0.755        0.761
   60  37139    1580    0.728 0.00182        0.725        0.732
  120  14768    4430    0.618 0.00220        0.614        0.622




   
## Univariable Cox Proportional Hazard Model for:  TNM_CLIN_N
X matrix deemed to be singular; variable 10 11 12
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_N, data = data)

  n= 262932, number of events= 57135 
   (6157 observations deleted due to missingness)

                  coef exp(coef) se(coef)       z Pr(>|z|)    
TNM_CLIN_Nc0  -0.85751   0.42422  0.03888 -22.053   <2e-16 ***
TNM_CLIN_Nc1   0.60835   1.83740  0.04406  13.808   <2e-16 ***
TNM_CLIN_Nc1A -0.06398   0.93802  0.06431  -0.995    0.320    
TNM_CLIN_Nc1B  0.83896   2.31396  0.05319  15.771   <2e-16 ***
TNM_CLIN_Nc2   0.69373   2.00117  0.05980  11.602   <2e-16 ***
TNM_CLIN_Nc2A  0.09198   1.09635  0.09177   1.002    0.316    
TNM_CLIN_Nc2B  0.82634   2.28494  0.06343  13.028   <2e-16 ***
TNM_CLIN_Nc2C  0.65866   1.93220  0.06716   9.807   <2e-16 ***
TNM_CLIN_Nc3   1.34482   3.83750  0.04638  28.996   <2e-16 ***
TNM_CLIN_Nc3A       NA        NA  0.00000      NA       NA    
TNM_CLIN_Nc3B       NA        NA  0.00000      NA       NA    
TNM_CLIN_Nc3C       NA        NA  0.00000      NA       NA    
TNM_CLIN_NcX  -0.37336   0.68842  0.03900  -9.574   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
TNM_CLIN_Nc0     0.4242     2.3573    0.3931    0.4578
TNM_CLIN_Nc1     1.8374     0.5442    1.6854    2.0031
TNM_CLIN_Nc1A    0.9380     1.0661    0.8269    1.0640
TNM_CLIN_Nc1B    2.3140     0.4322    2.0849    2.5682
TNM_CLIN_Nc2     2.0012     0.4997    1.7799    2.2500
TNM_CLIN_Nc2A    1.0963     0.9121    0.9159    1.3124
TNM_CLIN_Nc2B    2.2849     0.4376    2.0178    2.5874
TNM_CLIN_Nc2C    1.9322     0.5175    1.6939    2.2040
TNM_CLIN_Nc3     3.8375     0.2606    3.5040    4.2027
TNM_CLIN_Nc3A        NA         NA        NA        NA
TNM_CLIN_Nc3B        NA         NA        NA        NA
TNM_CLIN_Nc3C        NA         NA        NA        NA
TNM_CLIN_NcX     0.6884     1.4526    0.6378    0.7431

Concordance= 0.612  (se = 0.001 )
Rsquare= 0.042   (max possible= 0.994 )
Likelihood ratio test= 11286  on 10 df,   p=0
Wald test            = 15102  on 10 df,   p=0
Score (logrank) test = 18657  on 10 df,   p=0
Removed 4 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_CLIN_N
This manual palette can handle a maximum of 10 values. You have supplied 11.

Clinical Stage Group

uni_var(test_var = "TNM_CLIN_STAGE_GROUP", data_imp = data)
_________________________________________________
   
## TNM_CLIN_STAGE_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_STAGE_GROUP, data = data)

   30 observations deleted due to missingness 
                             n events median 0.95LCL 0.95UCL
TNM_CLIN_STAGE_GROUP=0    2401    385     NA  157.90      NA
TNM_CLIN_STAGE_GROUP=1    8331   1036     NA      NA      NA
TNM_CLIN_STAGE_GROUP=1A  68926   5518     NA      NA      NA
TNM_CLIN_STAGE_GROUP=1B  47613   5724     NA      NA      NA
TNM_CLIN_STAGE_GROUP=2    1514    423     NA  133.19      NA
TNM_CLIN_STAGE_GROUP=2A  16357   3911 145.54  135.33      NA
TNM_CLIN_STAGE_GROUP=2B  11360   4047  86.83   83.58   89.43
TNM_CLIN_STAGE_GROUP=2C   5992   3110  43.89   42.05   45.93
TNM_CLIN_STAGE_GROUP=3    8770   3832  67.55   62.78   72.64
TNM_CLIN_STAGE_GROUP=4   10283   8662   6.05    5.82    6.24
TNM_CLIN_STAGE_GROUP=4A      2      1  21.09   21.09      NA
TNM_CLIN_STAGE_GROUP=4C      1      1   3.88      NA      NA
TNM_CLIN_STAGE_GROUP=N_A  1583    684 131.02  113.08      NA
TNM_CLIN_STAGE_GROUP=99  85926  21015     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_STAGE_GROUP, data = data)

30 observations deleted due to missingness 
                TNM_CLIN_STAGE_GROUP=0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2059      70    0.969 0.00365        0.962        0.976
   24   1757      68    0.935 0.00536        0.925        0.946
   36   1429      61    0.900 0.00680        0.887        0.913
   48   1148      47    0.867 0.00804        0.852        0.883
   60    906      38    0.836 0.00925        0.818        0.854
  120    186      92    0.695 0.01679        0.662        0.728

                TNM_CLIN_STAGE_GROUP=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   7306     127    0.984 0.00143        0.981        0.987
   24   6641     159    0.962 0.00223        0.957        0.966
   36   5805     171    0.936 0.00293        0.930        0.941
   48   4959     123    0.914 0.00343        0.908        0.921
   60   4056     128    0.889 0.00402        0.881        0.897
  120    826     297    0.775 0.00770        0.760        0.790

                TNM_CLIN_STAGE_GROUP=1A 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  59184     610    0.990 0.000388        0.990        0.991
   24  52508     783    0.977 0.000621        0.975        0.978
   36  44403     821    0.960 0.000835        0.959        0.962
   48  36830     770    0.942 0.001044        0.940        0.944
   60  29704     611    0.925 0.001237        0.922        0.927
  120   5894    1709    0.829 0.002692        0.823        0.834

                TNM_CLIN_STAGE_GROUP=1B 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  40993     634    0.986 0.000569        0.984        0.987
   24  35300    1050    0.959 0.000987        0.957        0.961
   36  28360     994    0.929 0.001331        0.927        0.932
   48  22533     832    0.899 0.001648        0.896        0.902
   60  17258     658    0.870 0.001957        0.866        0.873
  120   2568    1426    0.732 0.004215        0.723        0.740

                TNM_CLIN_STAGE_GROUP=2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1310      70    0.951 0.00570        0.940        0.962
   24   1134      99    0.877 0.00889        0.860        0.894
   36    968      80    0.813 0.01075        0.792        0.834
   48    797      58    0.761 0.01205        0.737        0.785
   60    661      40    0.720 0.01302        0.695        0.746
  120     70      73    0.577 0.02045        0.538        0.618

                TNM_CLIN_STAGE_GROUP=2A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  14195     472    0.969 0.00140        0.966        0.972
   24  11965     849    0.908 0.00241        0.904        0.913
   36   9355     817    0.841 0.00319        0.834        0.847
   48   7220     583    0.783 0.00376        0.776        0.791
   60   5425     424    0.733 0.00425        0.724        0.741
  120    770     707    0.560 0.00742        0.546        0.575

                TNM_CLIN_STAGE_GROUP=2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   9757     598    0.944 0.00222        0.940        0.949
   24   7764    1079    0.835 0.00370        0.827        0.842
   36   5771     893    0.731 0.00459        0.722        0.740
   48   4322     517    0.660 0.00511        0.650        0.670
   60   3127     351    0.600 0.00555        0.590        0.611
  120    422     566    0.411 0.00850        0.395        0.428

                TNM_CLIN_STAGE_GROUP=2C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4868     731    0.873 0.00440        0.864        0.882
   24   3484     954    0.694 0.00625        0.682        0.706
   36   2413     612    0.563 0.00697        0.549        0.577
   48   1661     358    0.471 0.00734        0.457        0.486
   60   1151     181    0.414 0.00759        0.400        0.429
  120    119     266    0.251 0.01044        0.232        0.273

                TNM_CLIN_STAGE_GROUP=3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   7138    1066    0.874 0.00362        0.866        0.881
   24   5419    1135    0.729 0.00496        0.719        0.738
   36   4059     689    0.630 0.00554        0.619        0.641
   48   3116     395    0.564 0.00587        0.553        0.576
   60   2356     217    0.521 0.00612        0.509        0.533
  120    390     309    0.407 0.00808        0.392        0.423

                TNM_CLIN_STAGE_GROUP=4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3011    6768   0.3206 0.00471       0.3115        0.330
   24   1542    1204   0.1871 0.00404       0.1794        0.195
   36    900     391   0.1350 0.00368       0.1280        0.142
   48    598     142   0.1117 0.00353       0.1049        0.119
   60    414      57   0.0996 0.00350       0.0929        0.107
  120     61      91   0.0669 0.00392       0.0596        0.075

                TNM_CLIN_STAGE_GROUP=4A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0      1.0   0.000        1.000            1
   24      1       1      0.5   0.354        0.125            1

                TNM_CLIN_STAGE_GROUP=4C 
     time n.risk n.event survival std.err lower 95% CI upper 95% CI

                TNM_CLIN_STAGE_GROUP=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1286     216    0.860 0.00886        0.843        0.877
   24   1074     173    0.742 0.01130        0.720        0.764
   36    938      93    0.677 0.01217        0.653        0.701
   48    840      61    0.632 0.01265        0.607        0.657
   60    763      30    0.608 0.01287        0.584        0.634
  120    298      94    0.519 0.01404        0.492        0.547

                TNM_CLIN_STAGE_GROUP=99 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  73949    4010    0.951 0.000759        0.949        0.952
   24  65067    4095    0.896 0.001093        0.894        0.898
   36  56386    3248    0.849 0.001309        0.847        0.852
   48  49194    2357    0.812 0.001459        0.809        0.815
   60  42422    1837    0.780 0.001581        0.777        0.783
  120  14873    4715    0.662 0.002125        0.658        0.667




   
## Univariable Cox Proportional Hazard Model for:  TNM_CLIN_STAGE_GROUP
X matrix deemed to be singular; variable 4 10 11 12 15
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_STAGE_GROUP, data = data)

  n= 269059, number of events= 58349 
   (30 observations deleted due to missingness)

                            coef exp(coef) se(coef)       z Pr(>|z|)    
TNM_CLIN_STAGE_GROUP1   -0.39204   0.67568  0.05969  -6.568 5.10e-11 ***
TNM_CLIN_STAGE_GROUP1A  -0.76156   0.46694  0.05271 -14.447  < 2e-16 ***
TNM_CLIN_STAGE_GROUP1B  -0.25897   0.77185  0.05265  -4.918 8.72e-07 ***
TNM_CLIN_STAGE_GROUP1C        NA        NA  0.00000      NA       NA    
TNM_CLIN_STAGE_GROUP2    0.51512   1.67384  0.07044   7.313 2.61e-13 ***
TNM_CLIN_STAGE_GROUP2A   0.46412   1.59061  0.05342   8.689  < 2e-16 ***
TNM_CLIN_STAGE_GROUP2B   0.94890   2.58286  0.05334  17.790  < 2e-16 ***
TNM_CLIN_STAGE_GROUP2C   1.49831   4.47413  0.05404  27.725  < 2e-16 ***
TNM_CLIN_STAGE_GROUP3    1.19754   3.31196  0.05347  22.398  < 2e-16 ***
TNM_CLIN_STAGE_GROUP3A        NA        NA  0.00000      NA       NA    
TNM_CLIN_STAGE_GROUP3B        NA        NA  0.00000      NA       NA    
TNM_CLIN_STAGE_GROUP3C        NA        NA  0.00000      NA       NA    
TNM_CLIN_STAGE_GROUP4    3.02004  20.49201  0.05218  57.876  < 2e-16 ***
TNM_CLIN_STAGE_GROUP4A   1.67416   5.33429  1.00131   1.672   0.0945 .  
TNM_CLIN_STAGE_GROUP4B        NA        NA  0.00000      NA       NA    
TNM_CLIN_STAGE_GROUP4C   4.37109  79.13019  1.00132   4.365 1.27e-05 ***
TNM_CLIN_STAGE_GROUPN_A  0.83836   2.31257  0.06373  13.155  < 2e-16 ***
TNM_CLIN_STAGE_GROUP99   0.24306   1.27514  0.05144   4.725 2.30e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                        exp(coef) exp(-coef) lower .95 upper .95
TNM_CLIN_STAGE_GROUP1      0.6757    1.48000    0.6011    0.7595
TNM_CLIN_STAGE_GROUP1A     0.4669    2.14162    0.4211    0.5178
TNM_CLIN_STAGE_GROUP1B     0.7718    1.29559    0.6962    0.8558
TNM_CLIN_STAGE_GROUP1C         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP2      1.6738    0.59743    1.4580    1.9216
TNM_CLIN_STAGE_GROUP2A     1.5906    0.62869    1.4325    1.7662
TNM_CLIN_STAGE_GROUP2B     2.5829    0.38717    2.3265    2.8675
TNM_CLIN_STAGE_GROUP2C     4.4741    0.22351    4.0245    4.9740
TNM_CLIN_STAGE_GROUP3      3.3120    0.30194    2.9825    3.6779
TNM_CLIN_STAGE_GROUP3A         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP3B         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP3C         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP4     20.4920    0.04880   18.4998   22.6987
TNM_CLIN_STAGE_GROUP4A     5.3343    0.18747    0.7495   37.9655
TNM_CLIN_STAGE_GROUP4B         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP4C    79.1302    0.01264   11.1179  563.2010
TNM_CLIN_STAGE_GROUPN_A    2.3126    0.43242    2.0410    2.6202
TNM_CLIN_STAGE_GROUP99     1.2751    0.78423    1.1528    1.4104

Concordance= 0.733  (se = 0.001 )
Rsquare= 0.167   (max possible= 0.994 )
Likelihood ratio test= 49026  on 13 df,   p=0
Wald test            = 67820  on 13 df,   p=0
Score (logrank) test = 124787  on 13 df,   p=0
Removed 6 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_CLIN_STAGE_GROUP
This manual palette can handle a maximum of 10 values. You have supplied 14.

Pathologic T Stage

uni_var(test_var = "TNM_PATH_T", data_imp = data)
_________________________________________________
   
## TNM_PATH_T
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_T, data = data)

   11122 observations deleted due to missingness 
                   n events median 0.95LCL 0.95UCL
TNM_PATH_T=N_A  1571    676  132.6   115.9      NA
TNM_PATH_T=p0   3652    695     NA   151.8      NA
TNM_PATH_T=p1   8932   1030     NA      NA      NA
TNM_PATH_T=p1A 43424   2223     NA    95.5      NA
TNM_PATH_T=p1B 17703   1125   97.1      NA      NA
TNM_PATH_T=p2   2844    583     NA      NA      NA
TNM_PATH_T=p2A 30821   4110     NA      NA      NA
TNM_PATH_T=p2B  6903   1546  148.2   144.0      NA
TNM_PATH_T=p3   1857    660  113.3    98.9   131.8
TNM_PATH_T=p3A 12774   3101  146.9   137.2      NA
TNM_PATH_T=p3B  9644   3474   85.3    81.4    89.2
TNM_PATH_T=p4   1215    612   51.8    46.3    58.6
TNM_PATH_T=p4A  6629   2464   80.7    74.5    86.5
TNM_PATH_T=p4B 11783   6564   35.9    34.9    37.4
TNM_PATH_T=pIS   936    133     NA   151.2      NA
TNM_PATH_T=pX  97279  25883     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_T, data = data)

11122 observations deleted due to missingness 
                TNM_PATH_T=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1280     210    0.863 0.00882        0.845        0.880
   24   1070     171    0.745 0.01130        0.723        0.768
   36    935      93    0.679 0.01219        0.656        0.704
   48    837      61    0.634 0.01268        0.610        0.659
   60    761      30    0.611 0.01291        0.586        0.637
  120    298      94    0.521 0.01409        0.494        0.549

                TNM_PATH_T=p0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2852     333    0.904 0.00503        0.894        0.913
   24   2202     145    0.854 0.00622        0.842        0.866
   36   1611      84    0.817 0.00714        0.803        0.831
   48   1179      45    0.792 0.00788        0.776        0.807
   60    796      33    0.765 0.00885        0.748        0.783
  120     93      50    0.654 0.01954        0.617        0.693

                TNM_PATH_T=p1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   7787     149    0.982 0.00145        0.979        0.985
   24   7087     161    0.961 0.00217        0.957        0.965
   36   6260     143    0.941 0.00272        0.935        0.946
   48   5320     131    0.920 0.00322        0.913        0.926
   60   4352     127    0.896 0.00378        0.888        0.903
  120    974     280    0.793 0.00715        0.779        0.807

                TNM_PATH_T=p1A 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  36103     426    0.989 0.000524        0.988        0.990
   24  30011     468    0.975 0.000822        0.974        0.977
   36  22342     442    0.959 0.001122        0.957        0.961
   48  15518     386    0.939 0.001484        0.936        0.942
   60   9564     257    0.920 0.001891        0.916        0.923

                TNM_PATH_T=p1B 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  14777     198    0.988 0.000869        0.986        0.989
   24  12032     279    0.967 0.001475        0.965        0.970
   36   8520     258    0.943 0.002074        0.939        0.947
   48   5695     183    0.919 0.002693        0.914        0.924
   60   3327     100    0.898 0.003339        0.892        0.905

                TNM_PATH_T=p2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2485      72    0.973 0.00314        0.967        0.979
   24   2214     107    0.930 0.00509        0.920        0.940
   36   1971      87    0.892 0.00629        0.879        0.904
   48   1689      78    0.855 0.00730        0.840        0.869
   60   1429      71    0.816 0.00827        0.800        0.833
  120    309     156    0.670 0.01347        0.644        0.697

                TNM_PATH_T=p2A 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  26767     453    0.984 0.000738        0.983        0.986
   24  23028     748    0.955 0.001273        0.953        0.957
   36  18498     725    0.922 0.001720        0.919        0.925
   48  14682     600    0.889 0.002118        0.885        0.893
   60  11220     468    0.858 0.002503        0.853        0.862
  120   2202     997    0.721 0.004842        0.712        0.731

                TNM_PATH_T=p2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   5902     239    0.963 0.00236        0.958        0.967
   24   4941     323    0.907 0.00374        0.900        0.915
   36   3835     313    0.844 0.00490        0.835        0.854
   48   2920     231    0.788 0.00580        0.777        0.800
   60   2160     143    0.745 0.00652        0.732        0.758
  120    389     267    0.579 0.01120        0.558        0.602

                TNM_PATH_T=p3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1600     105    0.941 0.00561        0.930        0.952
   24   1369     141    0.856 0.00854        0.839        0.872
   36   1103     140    0.764 0.01056        0.744        0.785
   48    918      77    0.708 0.01157        0.686        0.731
   60    714      80    0.642 0.01266        0.617        0.667
  120    130     108    0.482 0.01782        0.449        0.519

                TNM_PATH_T=p3A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  11076     390    0.967 0.00162        0.964        0.971
   24   9297     679    0.905 0.00277        0.900        0.911
   36   7158     656    0.835 0.00367        0.828        0.842
   48   5502     442    0.778 0.00430        0.770        0.787
   60   4064     336    0.725 0.00489        0.716        0.735
  120    795     548    0.555 0.00802        0.539        0.571

                TNM_PATH_T=p3B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   8268     553    0.940 0.00249        0.935        0.945
   24   6499     953    0.826 0.00410        0.818        0.834
   36   4695     811    0.714 0.00510        0.704        0.724
   48   3423     414    0.644 0.00564        0.633        0.655
   60   2388     286    0.584 0.00615        0.572        0.596
  120    404     424    0.406 0.00916        0.388        0.424

                TNM_PATH_T=p4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    951     193    0.836  0.0108        0.815        0.857
   24    725     153    0.696  0.0137        0.670        0.724
   36    575      90    0.606  0.0149        0.578        0.636
   48    439      78    0.520  0.0156        0.490        0.551
   60    335      45    0.462  0.0161        0.432        0.495
  120     61      48    0.345  0.0204        0.307        0.388

                TNM_PATH_T=p4A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   5585     489    0.922 0.00337        0.916        0.929
   24   4289     710    0.799 0.00522        0.789        0.809
   36   3172     488    0.700 0.00620        0.688        0.712
   48   2352     297    0.629 0.00683        0.615        0.642
   60   1681     185    0.573 0.00736        0.559        0.588
  120    298     283    0.415 0.01039        0.395        0.436

                TNM_PATH_T=p4B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   9011    2032    0.821 0.00361        0.814        0.828
   24   6154    2019    0.628 0.00467        0.619        0.637
   36   4057    1157    0.500 0.00502        0.490        0.510
   48   2742     589    0.419 0.00520        0.409        0.430
   60   1834     319    0.364 0.00537        0.354        0.375
  120    222     429    0.220 0.00715        0.206        0.234

                TNM_PATH_T=pIS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    800      15    0.983 0.00445        0.974        0.991
   24    692      20    0.957 0.00718        0.943        0.971
   36    545      24    0.919 0.01019        0.900        0.940
   48    433      20    0.883 0.01267        0.858        0.908
   60    322      11    0.857 0.01452        0.829        0.886
  120     67      42    0.660 0.03142        0.601        0.724

                TNM_PATH_T=pX 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  82196    7397    0.921 0.000882        0.919        0.923
   24  75740    3959    0.876 0.001092        0.874        0.878
   36  70490    3062    0.840 0.001226        0.838        0.842
   48  65737    2409    0.811 0.001320        0.808        0.813
   60  60722    1950    0.786 0.001392        0.783        0.789
  120  20235    6160    0.681 0.001783        0.677        0.684




   
## Univariable Cox Proportional Hazard Model for:  TNM_PATH_T
X matrix deemed to be singular; variable 5 6 16 17 18
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_T, data = data)

  n= 257967, number of events= 54879 
   (11122 observations deleted due to missingness)

                   coef exp(coef) se(coef)       z Pr(>|z|)    
TNM_PATH_Tp0   -0.40264   0.66855  0.05407  -7.447 9.55e-14 ***
TNM_PATH_Tp1   -1.30100   0.27226  0.04951 -26.277  < 2e-16 ***
TNM_PATH_Tp1A  -1.74878   0.17399  0.04401 -39.733  < 2e-16 ***
TNM_PATH_Tp1B  -1.49499   0.22425  0.04875 -30.664  < 2e-16 ***
TNM_PATH_Tp1C        NA        NA  0.00000      NA       NA    
TNM_PATH_Tp1MI       NA        NA  0.00000      NA       NA    
TNM_PATH_Tp2   -0.73383   0.48007  0.05653 -12.981  < 2e-16 ***
TNM_PATH_Tp2A  -1.02393   0.35918  0.04153 -24.655  < 2e-16 ***
TNM_PATH_Tp2B  -0.43444   0.64763  0.04614  -9.415  < 2e-16 ***
TNM_PATH_Tp3   -0.05096   0.95031  0.05474  -0.931 0.351861    
TNM_PATH_Tp3A  -0.37181   0.68948  0.04248  -8.753  < 2e-16 ***
TNM_PATH_Tp3B   0.12594   1.13422  0.04209   2.992 0.002768 ** 
TNM_PATH_Tp4    0.48632   1.62632  0.05582   8.712  < 2e-16 ***
TNM_PATH_Tp4A   0.16855   1.18358  0.04346   3.878 0.000105 ***
TNM_PATH_Tp4B   0.79713   2.21916  0.04048  19.692  < 2e-16 ***
TNM_PATH_Tp4C        NA        NA  0.00000      NA       NA    
TNM_PATH_Tp4D        NA        NA  0.00000      NA       NA    
TNM_PATH_TpA         NA        NA  0.00000      NA       NA    
TNM_PATH_TpIS  -0.94105   0.39022  0.09487  -9.919  < 2e-16 ***
TNM_PATH_TpX   -0.62129   0.53725  0.03896 -15.946  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

               exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_Tp0      0.6686     1.4958    0.6013    0.7433
TNM_PATH_Tp1      0.2723     3.6730    0.2471    0.3000
TNM_PATH_Tp1A     0.1740     5.7476    0.1596    0.1897
TNM_PATH_Tp1B     0.2243     4.4593    0.2038    0.2467
TNM_PATH_Tp1C         NA         NA        NA        NA
TNM_PATH_Tp1MI        NA         NA        NA        NA
TNM_PATH_Tp2      0.4801     2.0830    0.4297    0.5363
TNM_PATH_Tp2A     0.3592     2.7841    0.3311    0.3896
TNM_PATH_Tp2B     0.6476     1.5441    0.5916    0.7089
TNM_PATH_Tp3      0.9503     1.0523    0.8536    1.0579
TNM_PATH_Tp3A     0.6895     1.4504    0.6344    0.7493
TNM_PATH_Tp3B     1.1342     0.8817    1.0444    1.2317
TNM_PATH_Tp4      1.6263     0.6149    1.4578    1.8144
TNM_PATH_Tp4A     1.1836     0.8449    1.0869    1.2888
TNM_PATH_Tp4B     2.2192     0.4506    2.0499    2.4024
TNM_PATH_Tp4C         NA         NA        NA        NA
TNM_PATH_Tp4D         NA         NA        NA        NA
TNM_PATH_TpA          NA         NA        NA        NA
TNM_PATH_TpIS     0.3902     2.5627    0.3240    0.4700
TNM_PATH_TpX      0.5373     1.8613    0.4978    0.5799

Concordance= 0.661  (se = 0.001 )
Rsquare= 0.072   (max possible= 0.994 )
Likelihood ratio test= 19392  on 15 df,   p=0
Wald test            = 20756  on 15 df,   p=0
Score (logrank) test = 25563  on 15 df,   p=0
Removed 6 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_T
This manual palette can handle a maximum of 10 values. You have supplied 16.

Pathologic N Stage

uni_var(test_var = "TNM_PATH_N", data_imp = data)
_________________________________________________
   
## TNM_PATH_N
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_N, data = data)

   20664 observations deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
TNM_PATH_N=N_A   1569    674  132.6   116.9      NA
TNM_PATH_N=p0  121211  17292     NA      NA      NA
TNM_PATH_N=p1    3607   1406   94.1    82.4   109.5
TNM_PATH_N=p1A   8421   2173  157.0   144.0      NA
TNM_PATH_N=p1B   1951    895   53.1    44.7    60.0
TNM_PATH_N=p2    1104    537   53.5    47.4    62.4
TNM_PATH_N=p2A   2858    966   98.8    80.4   113.1
TNM_PATH_N=p2B   1480    713   43.1    37.7    51.9
TNM_PATH_N=p2C   1242    555   54.6    49.4    61.6
TNM_PATH_N=p3    3849   2373   25.4    24.3    27.0
TNM_PATH_N=pX  101133  26327     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_N, data = data)

20664 observations deleted due to missingness 
                TNM_PATH_N=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1280     208    0.864 0.00879        0.847        0.881
   24   1070     171    0.746 0.01129        0.724        0.769
   36    935      93    0.680 0.01219        0.657        0.705
   48    837      61    0.635 0.01269        0.611        0.660
   60    761      30    0.612 0.01292        0.587        0.637
  120    298      94    0.522 0.01410        0.495        0.550

                TNM_PATH_N=p0 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 104642    2132    0.981 0.000407        0.980        0.982
   24  91086    3233    0.949 0.000678        0.948        0.950
   36  74985    3218    0.913 0.000905        0.911        0.915
   48  61113    2325    0.882 0.001076        0.880        0.884
   60  48126    1834    0.853 0.001240        0.850        0.855
  120  10572    4081    0.729 0.002223        0.725        0.734

                TNM_PATH_N=p1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2943     395    0.886 0.00542        0.875        0.896
   24   2347     348    0.777 0.00726        0.762        0.791
   36   1857     250    0.690 0.00827        0.674        0.706
   48   1459     165    0.625 0.00891        0.608        0.642
   60   1100     105    0.575 0.00945        0.556        0.594
  120    195     126    0.462 0.01265        0.438        0.488

                TNM_PATH_N=p1A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   7423     273    0.966 0.00204        0.962        0.970
   24   6066     593    0.885 0.00370        0.877        0.892
   36   4611     472    0.809 0.00474        0.800        0.819
   48   3486     328    0.746 0.00551        0.736        0.757
   60   2529     211    0.696 0.00615        0.684        0.708
  120    493     273    0.561 0.00956        0.543        0.580

                TNM_PATH_N=p1B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1516     279    0.850 0.00829        0.834        0.866
   24   1086     290    0.680 0.01114        0.658        0.702
   36    754     166    0.568 0.01226        0.544        0.592
   48    553      66    0.513 0.01281        0.488        0.539
   60    395      38    0.473 0.01335        0.448        0.500
  120     67      50    0.368 0.01782        0.335        0.405

                TNM_PATH_N=p2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    904     134    0.874  0.0102        0.854        0.894
   24    677     153    0.720  0.0141        0.693        0.748
   36    521      93    0.617  0.0156        0.587        0.648
   48    391      72    0.526  0.0166        0.495        0.560
   60    301      40    0.469  0.0171        0.436        0.503
  120     64      37    0.373  0.0210        0.334        0.417

                TNM_PATH_N=p2A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2485     130    0.952 0.00411        0.944        0.960
   24   1937     307    0.828 0.00751        0.814        0.843
   36   1389     221    0.725 0.00926        0.707        0.743
   48   1012     133    0.649 0.01038        0.629        0.670
   60    722      69    0.600 0.01118        0.578        0.622
  120    131     103    0.451 0.01690        0.419        0.485

                TNM_PATH_N=p2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1151     226    0.841 0.00969        0.823        0.861
   24    798     233    0.662 0.01293        0.637        0.688
   36    537     136    0.540 0.01422        0.512        0.568
   48    378      55    0.478 0.01482        0.450        0.508
   60    264      25    0.442 0.01539        0.413        0.473
  120     51      34    0.349 0.01964        0.313        0.390

                TNM_PATH_N=p2C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    995     145    0.878 0.00949        0.860        0.897
   24    708     175    0.715 0.01359        0.688        0.742
   36    501      87    0.617 0.01526        0.588        0.648
   48    349      61    0.535 0.01649        0.503        0.568
   60    244      40    0.467 0.01756        0.434        0.503
  120     45      45    0.328 0.02297        0.286        0.376

                TNM_PATH_N=p3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2717     984    0.739 0.00717        0.725        0.753
   24   1693     770    0.519 0.00835        0.503        0.536
   36   1103     327    0.410 0.00851        0.394        0.427
   48    735     149    0.348 0.00863        0.332        0.365
   60    510      68    0.312 0.00878        0.295        0.330
  120     80      68    0.242 0.01072        0.222        0.264

                TNM_PATH_N=pX 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  83586    8073    0.916 0.000891        0.915        0.918
   24  73950    4492    0.866 0.001119        0.863        0.868
   36  64734    3334    0.825 0.001269        0.822        0.827
   48  56651    2514    0.791 0.001383        0.789        0.794
   60  49360    1906    0.763 0.001475        0.761        0.766
  120  14481    5267    0.649 0.001993        0.645        0.653




   
## Univariable Cox Proportional Hazard Model for:  TNM_PATH_N
X matrix deemed to be singular; variable 2 3 4 5 9 10 16 17 18
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_N, data = data)

  n= 248425, number of events= 53911 
   (20664 observations deleted due to missingness)

                   coef exp(coef) se(coef)       z Pr(>|z|)    
TNM_PATH_Np0   -0.98982   0.37164  0.03928 -25.198  < 2e-16 ***
TNM_PATH_Np0I-       NA        NA  0.00000      NA       NA    
TNM_PATH_Np0I+       NA        NA  0.00000      NA       NA    
TNM_PATH_Np0M-       NA        NA  0.00000      NA       NA    
TNM_PATH_Np0M+       NA        NA  0.00000      NA       NA    
TNM_PATH_Np1    0.17018   1.18552  0.04688   3.630 0.000283 ***
TNM_PATH_Np1A  -0.28891   0.74908  0.04412  -6.548 5.84e-11 ***
TNM_PATH_Np1B   0.52071   1.68322  0.05105  10.200  < 2e-16 ***
TNM_PATH_Np1C        NA        NA  0.00000      NA       NA    
TNM_PATH_Np1MI       NA        NA  0.00000      NA       NA    
TNM_PATH_Np2    0.43648   1.54725  0.05787   7.542 4.62e-14 ***
TNM_PATH_Np2A   0.05822   1.05995  0.05023   1.159 0.246383    
TNM_PATH_Np2B   0.59816   1.81878  0.05377  11.124  < 2e-16 ***
TNM_PATH_Np2C   0.47071   1.60112  0.05736   8.206 2.22e-16 ***
TNM_PATH_Np3    1.00503   2.73199  0.04373  22.981  < 2e-16 ***
TNM_PATH_Np3A        NA        NA  0.00000      NA       NA    
TNM_PATH_Np3B        NA        NA  0.00000      NA       NA    
TNM_PATH_Np3C        NA        NA  0.00000      NA       NA    
TNM_PATH_NpX   -0.48807   0.61381  0.03901 -12.511  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

               exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_Np0      0.3716     2.6908    0.3441    0.4014
TNM_PATH_Np0I-        NA         NA        NA        NA
TNM_PATH_Np0I+        NA         NA        NA        NA
TNM_PATH_Np0M-        NA         NA        NA        NA
TNM_PATH_Np0M+        NA         NA        NA        NA
TNM_PATH_Np1      1.1855     0.8435    1.0815    1.2996
TNM_PATH_Np1A     0.7491     1.3350    0.6870    0.8167
TNM_PATH_Np1B     1.6832     0.5941    1.5230    1.8603
TNM_PATH_Np1C         NA         NA        NA        NA
TNM_PATH_Np1MI        NA         NA        NA        NA
TNM_PATH_Np2      1.5472     0.6463    1.3813    1.7331
TNM_PATH_Np2A     1.0600     0.9434    0.9606    1.1696
TNM_PATH_Np2B     1.8188     0.5498    1.6368    2.0209
TNM_PATH_Np2C     1.6011     0.6246    1.4309    1.7916
TNM_PATH_Np3      2.7320     0.3660    2.5076    2.9765
TNM_PATH_Np3A         NA         NA        NA        NA
TNM_PATH_Np3B         NA         NA        NA        NA
TNM_PATH_Np3C         NA         NA        NA        NA
TNM_PATH_NpX      0.6138     1.6292    0.5686    0.6626

Concordance= 0.623  (se = 0.001 )
Rsquare= 0.043   (max possible= 0.994 )
Likelihood ratio test= 10899  on 10 df,   p=0
Wald test            = 13404  on 10 df,   p=0
Score (logrank) test = 15807  on 10 df,   p=0
Removed 10 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_N
This manual palette can handle a maximum of 10 values. You have supplied 11.

Pathologic M Stage

uni_var(test_var = "TNM_PATH_M", data_imp = data)
_________________________________________________
   
## TNM_PATH_M
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_M, data = data)

   146631 observations deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
TNM_PATH_M=N_A   1552    660 132.80  119.59      NA
TNM_PATH_M=p1    1529   1329   7.13    6.67    7.85
TNM_PATH_M=p1A    828    512  22.74   19.58   26.41
TNM_PATH_M=p1B    626    501  10.15    8.54   12.32
TNM_PATH_M=p1C   1687   1440   5.75    5.42    6.21
TNM_PATH_M=pX  116236  31810     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_M, data = data)

146631 observations deleted due to missingness 
                TNM_PATH_M=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1268     203    0.865 0.00879        0.848        0.883
   24   1065     164    0.751 0.01128        0.730        0.774
   36    935      91    0.686 0.01219        0.663        0.711
   48    838      60    0.641 0.01271        0.617        0.667
   60    761      31    0.617 0.01296        0.592        0.643
  120    298      94    0.526 0.01417        0.499        0.555

                TNM_PATH_M=p1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    483     996   0.3333 0.01225       0.3102        0.358
   24    270     197   0.1949 0.01041       0.1755        0.216
   36    188      70   0.1430 0.00931       0.1258        0.162
   48    149      23   0.1246 0.00887       0.1084        0.143
   60    127      12   0.1142 0.00862       0.0985        0.132
  120     32      26   0.0864 0.00818       0.0718        0.104

                TNM_PATH_M=p1A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    504     264    0.668  0.0168        0.636        0.701
   24    325     129    0.489  0.0183        0.454        0.526
   36    219      67    0.380  0.0185        0.345        0.417
   48    169      20    0.342  0.0184        0.308        0.380
   60    137       6    0.329  0.0185        0.295        0.367
  120     29      24    0.241  0.0214        0.202        0.287

                TNM_PATH_M=p1B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    274     324   0.4692  0.0203       0.4310        0.511
   24    145     105   0.2826  0.0187       0.2482        0.322
   36     76      42   0.1923  0.0172       0.1614        0.229
   48     48      15   0.1509  0.0165       0.1217        0.187
   60     29       8   0.1215  0.0163       0.0934        0.158
  120      4       5   0.0926  0.0168       0.0648        0.132

                TNM_PATH_M=p1C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    444    1171   0.2870 0.01127       0.2657       0.3099
   24    201     187   0.1580 0.00941       0.1406       0.1776
   36    100      51   0.1115 0.00865       0.0958       0.1298
   48     61      16   0.0918 0.00842       0.0767       0.1099
   60     36       9   0.0764 0.00845       0.0615       0.0949
  120      3       6   0.0547 0.00989       0.0384       0.0780

                TNM_PATH_M=pX 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 102381    5619    0.950 0.000653        0.949        0.951
   24  94717    5267    0.900 0.000909        0.898        0.902
   36  88366    4458    0.857 0.001068        0.855        0.859
   48  82765    3503    0.823 0.001173        0.821        0.825
   60  76890    2885    0.794 0.001251        0.791        0.796
  120  26111    8807    0.677 0.001613        0.673        0.680




   
## Univariable Cox Proportional Hazard Model for:  TNM_PATH_M

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_M, data = data)

  n= 122458, number of events= 36252 
   (146631 observations deleted due to missingness)

                  coef exp(coef) se(coef)      z Pr(>|z|)    
TNM_PATH_Mp1   1.90037   6.68839  0.04777  39.78   <2e-16 ***
TNM_PATH_Mp1A  1.03284   2.80903  0.05895  17.52   <2e-16 ***
TNM_PATH_Mp1B  1.79045   5.99215  0.05943  30.13   <2e-16 ***
TNM_PATH_Mp1C  2.23370   9.33430  0.04739  47.14   <2e-16 ***
TNM_PATH_MpX  -0.61727   0.53942  0.03933 -15.69   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_Mp1     6.6884     0.1495    6.0906    7.3449
TNM_PATH_Mp1A    2.8090     0.3560    2.5025    3.1531
TNM_PATH_Mp1B    5.9921     0.1669    5.3333    6.7324
TNM_PATH_Mp1C    9.3343     0.1071    8.5064   10.2428
TNM_PATH_MpX     0.5394     1.8539    0.4994    0.5826

Concordance= 0.564  (se = 0 )
Rsquare= 0.09   (max possible= 0.999 )
Likelihood ratio test= 11597  on 5 df,   p=0
Wald test            = 20173  on 5 df,   p=0
Score (logrank) test = 34277  on 5 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_M

Pathologic Stage Group

uni_var(test_var = "TNM_PATH_STAGE_GROUP", data_imp = data)
_________________________________________________
   
## TNM_PATH_STAGE_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_STAGE_GROUP, data = data)

   8113 observations deleted due to missingness 
                             n events median 0.95LCL 0.95UCL
TNM_PATH_STAGE_GROUP=0    1898    267     NA      NA      NA
TNM_PATH_STAGE_GROUP=1    6027    735     NA      NA      NA
TNM_PATH_STAGE_GROUP=1A  63103   5381     NA      NA      NA
TNM_PATH_STAGE_GROUP=1B  53543   6282     NA      NA      NA
TNM_PATH_STAGE_GROUP=2    1254    357     NA  150.54      NA
TNM_PATH_STAGE_GROUP=2A  17789   4009 156.65  153.86      NA
TNM_PATH_STAGE_GROUP=2B  12097   4059 103.36   97.81  108.52
TNM_PATH_STAGE_GROUP=2C   6201   3093  49.94   47.90   52.80
TNM_PATH_STAGE_GROUP=3    5432   2327  91.99   83.91  102.08
TNM_PATH_STAGE_GROUP=3A   8067   1985     NA      NA      NA
TNM_PATH_STAGE_GROUP=3B   7374   2964  72.77   67.61   78.82
TNM_PATH_STAGE_GROUP=3C   5149   2928  31.87   30.49   33.54
TNM_PATH_STAGE_GROUP=4    5747   4687   8.08    7.75    8.54
TNM_PATH_STAGE_GROUP=4A      7      4  21.09   16.92      NA
TNM_PATH_STAGE_GROUP=4C      1      1   3.88      NA      NA
TNM_PATH_STAGE_GROUP=N_A  1576    678 132.63  115.88      NA
TNM_PATH_STAGE_GROUP=99  65711  16558     NA  164.47      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_STAGE_GROUP, data = data)

8113 observations deleted due to missingness 
                TNM_PATH_STAGE_GROUP=0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1624      37    0.979 0.00340        0.972        0.986
   24   1387      43    0.952 0.00529        0.941        0.962
   36   1094      45    0.917 0.00722        0.903        0.931
   48    896      28    0.892 0.00843        0.875        0.908
   60    692      26    0.863 0.00990        0.844        0.882
  120    169      82    0.695 0.01970        0.658        0.735

                TNM_PATH_STAGE_GROUP=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   5319      82    0.986 0.00159        0.982        0.989
   24   4896      90    0.968 0.00238        0.964        0.973
   36   4341     112    0.945 0.00318        0.939        0.951
   48   3785      86    0.925 0.00376        0.918        0.933
   60   3154      93    0.901 0.00445        0.892        0.909
  120    933     236    0.795 0.00786        0.780        0.811

                TNM_PATH_STAGE_GROUP=1A 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  54533     531    0.991 0.000393        0.990        0.992
   24  48988     668    0.978 0.000624        0.977        0.979
   36  42352     717    0.963 0.000836        0.961        0.965
   48  36073     698    0.946 0.001041        0.944        0.948
   60  30049     590    0.929 0.001231        0.927        0.932
  120   7927    1864    0.834 0.002492        0.830        0.839

                TNM_PATH_STAGE_GROUP=1B 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  46550     551    0.989 0.000470        0.988        0.990
   24  40904     914    0.968 0.000814        0.967        0.970
   36  33979    1023    0.942 0.001132        0.940        0.944
   48  27960     880    0.916 0.001411        0.913        0.918
   60  22421     753    0.889 0.001680        0.885        0.892
  120   5205    1908    0.764 0.003200        0.758        0.771

                TNM_PATH_STAGE_GROUP=2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1106      39    0.967 0.00518        0.957        0.977
   24    982      71    0.903 0.00878        0.886        0.921
   36    861      65    0.842 0.01100        0.821        0.864
   48    760      44    0.797 0.01231        0.773        0.822
   60    642      46    0.746 0.01362        0.720        0.774
  120    149      86    0.599 0.01886        0.563        0.637

                TNM_PATH_STAGE_GROUP=2A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  15546     384    0.977 0.00117        0.975        0.979
   24  13467     712    0.930 0.00204        0.926        0.934
   36  10896     784    0.872 0.00279        0.866        0.877
   48   8765     595    0.820 0.00333        0.814        0.827
   60   6919     468    0.773 0.00379        0.765        0.780
  120   1484     960    0.601 0.00611        0.589        0.613

                TNM_PATH_STAGE_GROUP=2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  10557     492    0.957 0.00189        0.953        0.961
   24   8692     946    0.867 0.00327        0.861        0.874
   36   6655     910    0.770 0.00421        0.761        0.778
   48   5174     553    0.701 0.00475        0.692        0.710
   60   3918     377    0.645 0.00518        0.635        0.655
  120    788     722    0.460 0.00737        0.446        0.475

                TNM_PATH_STAGE_GROUP=2C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   5191     578    0.903 0.00385        0.895        0.910
   24   3817     922    0.735 0.00590        0.723        0.746
   36   2725     616    0.608 0.00675        0.595        0.621
   48   1925     389    0.513 0.00722        0.499        0.527
   60   1397     214    0.450 0.00751        0.436        0.465
  120    207     355    0.274 0.00946        0.256        0.293

                TNM_PATH_STAGE_GROUP=3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4633     445    0.915 0.00387        0.907        0.922
   24   3691     686    0.775 0.00590        0.764        0.787
   36   2988     426    0.682 0.00670        0.669        0.696
   48   2452     275    0.617 0.00713        0.603        0.631
   60   2042     164    0.573 0.00740        0.559        0.588
  120    497     295    0.453 0.00892        0.436        0.471

                TNM_PATH_STAGE_GROUP=3A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   7218     190    0.975 0.00178        0.972        0.979
   24   6030     499    0.904 0.00348        0.898        0.911
   36   4725     432    0.834 0.00456        0.825        0.843
   48   3693     331    0.771 0.00538        0.761        0.782
   60   2815     205    0.724 0.00598        0.712        0.736
  120    598     302    0.597 0.00884        0.580        0.614

                TNM_PATH_STAGE_GROUP=3B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   6302     500    0.929 0.00308        0.923        0.935
   24   4819     906    0.789 0.00502        0.779        0.799
   36   3517     620    0.680 0.00595        0.668        0.691
   48   2630     354    0.605 0.00648        0.593        0.618
   60   1877     240    0.544 0.00693        0.531        0.558
  120    397     319    0.398 0.00921        0.381        0.417

                TNM_PATH_STAGE_GROUP=3C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3945     957    0.809 0.00557        0.798        0.820
   24   2595     982    0.598 0.00712        0.584        0.612
   36   1704     521    0.468 0.00752        0.454        0.483
   48   1145     236    0.396 0.00771        0.381        0.411
   60    794     108    0.354 0.00788        0.339        0.370
  120    129     117    0.265 0.00992        0.246        0.285

                TNM_PATH_STAGE_GROUP=4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2082    3410   0.3899 0.00656       0.3773        0.403
   24   1142     761   0.2416 0.00588       0.2304        0.253
   36    696     286   0.1760 0.00543       0.1657        0.187
   48    485     111   0.1457 0.00520       0.1358        0.156
   60    352      43   0.1315 0.00513       0.1218        0.142
  120     73      67   0.0975 0.00537       0.0875        0.109

                TNM_PATH_STAGE_GROUP=4A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       1    0.857   0.132        0.633            1
   24      3       3    0.429   0.187        0.182            1
   36      2       0    0.429   0.187        0.182            1
   48      2       0    0.429   0.187        0.182            1

                TNM_PATH_STAGE_GROUP=4C 
     time n.risk n.event survival std.err lower 95% CI upper 95% CI

                TNM_PATH_STAGE_GROUP=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1285     210    0.863 0.00879        0.846        0.880
   24   1073     173    0.745 0.01129        0.723        0.767
   36    937      93    0.679 0.01217        0.656        0.703
   48    839      61    0.634 0.01266        0.610        0.659
   60    763      30    0.611 0.01289        0.586        0.636
  120    298      94    0.521 0.01407        0.494        0.549

                TNM_PATH_STAGE_GROUP=99 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  53120    5868    0.906 0.00117        0.904        0.908
   24  46141    2896    0.855 0.00144        0.852        0.858
   36  39396    2010    0.815 0.00162        0.812        0.819
   48  33706    1462    0.783 0.00176        0.780        0.787
   60  28481    1103    0.756 0.00188        0.752        0.760
  120   7623    2840    0.643 0.00264        0.638        0.648




   
## Univariable Cox Proportional Hazard Model for:  TNM_PATH_STAGE_GROUP
X matrix deemed to be singular; variable 4 15
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_STAGE_GROUP, data = data)

  n= 260976, number of events= 56315 
   (8113 observations deleted due to missingness)

                            coef exp(coef) se(coef)       z Pr(>|z|)    
TNM_PATH_STAGE_GROUP1   -0.34791   0.70617  0.07146  -4.869 1.12e-06 ***
TNM_PATH_STAGE_GROUP1A  -0.63876   0.52795  0.06270 -10.187  < 2e-16 ***
TNM_PATH_STAGE_GROUP1B  -0.24798   0.78038  0.06249  -3.969 7.23e-05 ***
TNM_PATH_STAGE_GROUP1C        NA        NA  0.00000      NA       NA    
TNM_PATH_STAGE_GROUP2    0.53504   1.70753  0.08091   6.613 3.78e-11 ***
TNM_PATH_STAGE_GROUP2A   0.44226   1.55622  0.06320   6.997 2.61e-12 ***
TNM_PATH_STAGE_GROUP2B   0.92649   2.52563  0.06318  14.664  < 2e-16 ***
TNM_PATH_STAGE_GROUP2C   1.50887   4.52160  0.06379  23.652  < 2e-16 ***
TNM_PATH_STAGE_GROUP3    1.11948   3.06327  0.06462  17.325  < 2e-16 ***
TNM_PATH_STAGE_GROUP3A   0.57164   1.77118  0.06519   8.769  < 2e-16 ***
TNM_PATH_STAGE_GROUP3B   1.21380   3.36625  0.06390  18.995  < 2e-16 ***
TNM_PATH_STAGE_GROUP3C   1.81803   6.15971  0.06395  28.430  < 2e-16 ***
TNM_PATH_STAGE_GROUP4    2.87372  17.70272  0.06297  45.634  < 2e-16 ***
TNM_PATH_STAGE_GROUP4A   1.90116   6.69365  0.50374   3.774 0.000161 ***
TNM_PATH_STAGE_GROUP4B        NA        NA  0.00000      NA       NA    
TNM_PATH_STAGE_GROUP4C   4.44680  85.35367  1.00195   4.438 9.07e-06 ***
TNM_PATH_STAGE_GROUPN_A  0.96230   2.61770  0.07226  13.317  < 2e-16 ***
TNM_PATH_STAGE_GROUP99   0.52591   1.69200  0.06169   8.525  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                        exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_STAGE_GROUP1      0.7062    1.41610    0.6139    0.8123
TNM_PATH_STAGE_GROUP1A     0.5279    1.89413    0.4669    0.5970
TNM_PATH_STAGE_GROUP1B     0.7804    1.28143    0.6904    0.8820
TNM_PATH_STAGE_GROUP1C         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP2      1.7075    0.58564    1.4571    2.0010
TNM_PATH_STAGE_GROUP2A     1.5562    0.64258    1.3749    1.7615
TNM_PATH_STAGE_GROUP2B     2.5256    0.39594    2.2315    2.8586
TNM_PATH_STAGE_GROUP2C     4.5216    0.22116    3.9902    5.1238
TNM_PATH_STAGE_GROUP3      3.0633    0.32645    2.6989    3.4768
TNM_PATH_STAGE_GROUP3A     1.7712    0.56460    1.5587    2.0126
TNM_PATH_STAGE_GROUP3B     3.3663    0.29707    2.9700    3.8154
TNM_PATH_STAGE_GROUP3C     6.1597    0.16235    5.4341    6.9822
TNM_PATH_STAGE_GROUP4     17.7027    0.05649   15.6472   20.0282
TNM_PATH_STAGE_GROUP4A     6.6936    0.14940    2.4939   17.9658
TNM_PATH_STAGE_GROUP4B         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP4C    85.3537    0.01172   11.9774  608.2517
TNM_PATH_STAGE_GROUPN_A    2.6177    0.38201    2.2720    3.0160
TNM_PATH_STAGE_GROUP99     1.6920    0.59102    1.4993    1.9095

Concordance= 0.725  (se = 0.001 )
Rsquare= 0.132   (max possible= 0.994 )
Likelihood ratio test= 36976  on 16 df,   p=0
Wald test            = 44748  on 16 df,   p=0
Score (logrank) test = 69509  on 16 df,   p=0
Removed 3 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_STAGE_GROUP
This manual palette can handle a maximum of 10 values. You have supplied 17.

Margins

uni_var(test_var = "MARGINS", data_imp = data)
_________________________________________________
   
## MARGINS
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ MARGINS, data = data)

                               n events median 0.95LCL 0.95UCL
MARGINS=No Residual       244357  44121     NA      NA      NA
MARGINS=Residual, NOS       3777   1635  80.33   71.52    88.2
MARGINS=Microscopic Resid   3812   1645  76.94   70.34    88.5
MARGINS=Macroscopic Resid    296    198  20.80   17.35    25.9
MARGINS=Not evaluable        746    312  94.46   73.99   127.9
MARGINS=No surg            12757   9324   8.48    8.11     8.8
MARGINS=Unknown             3344   1125     NA  144.39      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ MARGINS, data = data)

                MARGINS=No Residual 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 210996    6551    0.971 0.000351        0.971        0.972
   24 183066    9236    0.927 0.000563        0.926        0.928
   36 152467    7722    0.885 0.000711        0.884        0.886
   48 126531    5573    0.850 0.000822        0.849        0.852
   60 102815    4203    0.820 0.000918        0.818        0.821
  120  25206    9615    0.698 0.001463        0.695        0.701

                MARGINS=Residual, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2825     645    0.821 0.00639        0.809        0.834
   24   2274     347    0.717 0.00766        0.702        0.732
   36   1839     219    0.644 0.00832        0.628        0.660
   48   1450     164    0.583 0.00879        0.566        0.600
   60   1181      75    0.551 0.00906        0.533        0.569
  120    250     175    0.424 0.01153        0.402        0.447

                MARGINS=Microscopic Resid 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2960     563    0.845 0.00600        0.834        0.857
   24   2365     378    0.733 0.00748        0.719        0.748
   36   1885     250    0.652 0.00823        0.636        0.668
   48   1533     149    0.598 0.00867        0.581        0.615
   60   1250     101    0.556 0.00902        0.538        0.574
  120    240     187    0.417 0.01206        0.394        0.441

                MARGINS=Macroscopic Resid 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    179     101    0.648  0.0283        0.595        0.706
   24    114      51    0.456  0.0301        0.401        0.519
   36     86      15    0.394  0.0300        0.339        0.457
   48     60      17    0.311  0.0297        0.258        0.375
   60     47       2    0.300  0.0297        0.247        0.364
  120      4      11    0.166  0.0394        0.104        0.264

                MARGINS=Not evaluable 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    569     122    0.829  0.0141        0.801        0.857
   24    448      77    0.712  0.0173        0.679        0.747
   36    369      39    0.647  0.0186        0.612        0.685
   48    305      18    0.613  0.0193        0.577        0.652
   60    244      17    0.576  0.0201        0.537        0.616
  120     63      35    0.458  0.0249        0.412        0.509

                MARGINS=No surg 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4872    7004    0.426 0.00451        0.418        0.435
   24   3117    1317    0.306 0.00429        0.298        0.314
   36   2173     484    0.255 0.00416        0.247        0.263
   48   1614     227    0.226 0.00411        0.218        0.234
   60   1229     111    0.209 0.00411        0.201        0.217
  120    267     164    0.165 0.00459        0.157        0.175

                MARGINS=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2681     392    0.877 0.00582        0.866        0.889
   24   2292     244    0.795 0.00727        0.781        0.809
   36   1979     141    0.744 0.00798        0.728        0.760
   48   1735      96    0.706 0.00845        0.690        0.723
   60   1484      65    0.678 0.00881        0.661        0.696
  120    447     160    0.571 0.01108        0.550        0.593




   
## Univariable Cox Proportional Hazard Model for:  MARGINS

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ MARGINS, data = data)

  n= 269089, number of events= 58360 

                             coef exp(coef) se(coef)      z Pr(>|z|)    
MARGINSResidual, NOS      1.09487   2.98881  0.02519  43.47   <2e-16 ***
MARGINSMicroscopic Resid  1.07437   2.92814  0.02512  42.77   <2e-16 ***
MARGINSMacroscopic Resid  1.94673   7.00576  0.07125  27.32   <2e-16 ***
MARGINSNot evaluable      1.01504   2.75946  0.05681  17.87   <2e-16 ***
MARGINSNo surg            2.40784  11.10996  0.01156 208.28   <2e-16 ***
MARGINSUnknown            0.62231   1.86323  0.03020  20.61   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                         exp(coef) exp(-coef) lower .95 upper .95
MARGINSResidual, NOS         2.989    0.33458     2.845     3.140
MARGINSMicroscopic Resid     2.928    0.34151     2.787     3.076
MARGINSMacroscopic Resid     7.006    0.14274     6.093     8.056
MARGINSNot evaluable         2.759    0.36239     2.469     3.085
MARGINSNo surg              11.110    0.09001    10.861    11.365
MARGINSUnknown               1.863    0.53670     1.756     1.977

Concordance= 0.629  (se = 0.001 )
Rsquare= 0.101   (max possible= 0.994 )
Likelihood ratio test= 28792  on 6 df,   p=0
Wald test            = 45274  on 6 df,   p=0
Score (logrank) test = 68916  on 6 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  MARGINS

Margins Yes/No

#uni_var(test_var = "MARGINS_YN", data_imp = data)

30 Day Readmission

uni_var(test_var = "READM_HOSP_30_DAYS_F", data_imp = data)
_________________________________________________
   
## READM_HOSP_30_DAYS_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ READM_HOSP_30_DAYS_F, data = data)

                                                n events median 0.95LCL 0.95UCL
READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit 257592  54886     NA      NA      NA
READM_HOSP_30_DAYS_F=Unplan_Readmit_Same     2551    852    130     117     152
READM_HOSP_30_DAYS_F=Plan_Readmit_Same       3999   1119     NA     157      NA
READM_HOSP_30_DAYS_F=PlanUnplan_Same          395     96     NA     122      NA
READM_HOSP_30_DAYS_F=9                       4552   1407     NA     160      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ READM_HOSP_30_DAYS_F, data = data)

                READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 215352   14575    0.940 0.000481        0.939        0.941
   24 185258   10909    0.890 0.000650        0.889        0.892
   36 153547    8354    0.847 0.000770        0.846        0.849
   48 127017    5856    0.813 0.000863        0.811        0.814
   60 102949    4317    0.783 0.000944        0.781        0.785
  120  24862    9659    0.665 0.001437        0.663        0.668

                READM_HOSP_30_DAYS_F=Unplan_Readmit_Same 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2035     264    0.891 0.00633        0.879        0.904
   24   1698     177    0.811 0.00815        0.795        0.827
   36   1426     108    0.756 0.00913        0.739        0.775
   48   1168      91    0.705 0.00998        0.686        0.725
   60    939      59    0.667 0.01061        0.646        0.688
  120    240     135    0.525 0.01435        0.498        0.554

                READM_HOSP_30_DAYS_F=Plan_Readmit_Same 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3450     180    0.952 0.00348        0.945        0.959
   24   2949     278    0.873 0.00556        0.862        0.884
   36   2504     184    0.816 0.00660        0.803        0.829
   48   2100     134    0.770 0.00734        0.755        0.784
   60   1731      90    0.734 0.00790        0.719        0.750
  120    520     226    0.598 0.01072        0.578        0.620

                READM_HOSP_30_DAYS_F=PlanUnplan_Same 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    344      21    0.944  0.0118        0.921        0.968
   24    304      15    0.902  0.0156        0.872        0.933
   36    242      21    0.834  0.0203        0.795        0.875
   48    201      13    0.786  0.0230        0.742        0.833
   60    166       7    0.758  0.0246        0.711        0.808
  120     18      17    0.617  0.0441        0.537        0.710

                READM_HOSP_30_DAYS_F=9 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3901     338    0.923 0.00404        0.915        0.931
   24   3467     271    0.857 0.00536        0.847        0.868
   36   3079     203    0.806 0.00615        0.794        0.818
   48   2742     150    0.765 0.00667        0.752        0.778
   60   2465     101    0.736 0.00702        0.722        0.750
  120    837     310    0.618 0.00866        0.601        0.635




   
## Univariable Cox Proportional Hazard Model for:  READM_HOSP_30_DAYS_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ READM_HOSP_30_DAYS_F, data = data)

  n= 269089, number of events= 58360 

                                           coef exp(coef) se(coef)      z Pr(>|z|)    
READM_HOSP_30_DAYS_FUnplan_Readmit_Same 0.51036   1.66590  0.03452 14.783  < 2e-16 ***
READM_HOSP_30_DAYS_FPlan_Readmit_Same   0.20743   1.23052  0.03020  6.869 6.47e-12 ***
READM_HOSP_30_DAYS_FPlanUnplan_Same     0.13446   1.14392  0.10215  1.316    0.188    
READM_HOSP_30_DAYS_F9                   0.18912   1.20819  0.02701  7.001 2.55e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                        exp(coef) exp(-coef) lower .95 upper .95
READM_HOSP_30_DAYS_FUnplan_Readmit_Same     1.666     0.6003    1.5569     1.783
READM_HOSP_30_DAYS_FPlan_Readmit_Same       1.231     0.8127    1.1598     1.306
READM_HOSP_30_DAYS_FPlanUnplan_Same         1.144     0.8742    0.9364     1.397
READM_HOSP_30_DAYS_F9                       1.208     0.8277    1.1459     1.274

Concordance= 0.507  (se = 0 )
Rsquare= 0.001   (max possible= 0.994 )
Likelihood ratio test= 271.2  on 4 df,   p=0
Wald test            = 306.7  on 4 df,   p=0
Score (logrank) test = 311.6  on 4 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  READM_HOSP_30_DAYS_F

Radiation Type

uni_var(test_var = "RX_SUMM_RADIATION_F", data_imp = data)
_________________________________________________
   
## RX_SUMM_RADIATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_SUMM_RADIATION_F, data = data)

                                                n events median 0.95LCL 0.95UCL
RX_SUMM_RADIATION_F=None                   258472  51549     NA      NA      NA
RX_SUMM_RADIATION_F=Beam Radiation           8638   6244   14.1    13.4    14.8
RX_SUMM_RADIATION_F=Radioactive Implants       68     42   29.7    23.4    64.3
RX_SUMM_RADIATION_F=Radioisotopes               5      3   44.4    12.4      NA
RX_SUMM_RADIATION_F=Beam + Imp or Isotopes     46     35   22.9    18.2    30.1
RX_SUMM_RADIATION_F=Radiation, NOS             85     58   18.6    11.9    27.8
RX_SUMM_RADIATION_F=Unknown                  1775    429     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_SUMM_RADIATION_F, data = data)

                RX_SUMM_RADIATION_F=None 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 218978   11287    0.954 0.000426        0.953        0.955
   24 189295   10376    0.907 0.000607        0.905        0.908
   36 157485    8250    0.864 0.000736        0.863        0.866
   48 130533    5965    0.829 0.000835        0.827        0.831
   60 106060    4381    0.799 0.000922        0.797        0.801
  120  25905   10033    0.679 0.001426        0.676        0.682

                RX_SUMM_RADIATION_F=Beam Radiation 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4407    3955    0.536 0.00542        0.525        0.547
   24   2878    1169    0.388 0.00539        0.377        0.398
   36   1974     544    0.309 0.00525        0.299        0.320
   48   1454     238    0.269 0.00518        0.259        0.279
   60   1067     142    0.240 0.00516        0.231        0.251
  120    196     179    0.175 0.00601        0.164        0.188

                RX_SUMM_RADIATION_F=Radioactive Implants 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     55      11    0.834  0.0457        0.749        0.929
   24     37      16    0.588  0.0610        0.480        0.720
   36     22      10    0.417  0.0630        0.310        0.561
   48     15       2    0.375  0.0635        0.269        0.522
   60     14       0    0.375  0.0635        0.269        0.522
  120      5       3    0.270  0.0691        0.164        0.446

                RX_SUMM_RADIATION_F=Radioisotopes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      3       1    0.800   0.179       0.5161            1
   24      2       1    0.533   0.248       0.2142            1
   36      2       0    0.533   0.248       0.2142            1
   48      1       1    0.267   0.226       0.0507            1
   60      1       0    0.267   0.226       0.0507            1

                RX_SUMM_RADIATION_F=Beam + Imp or Isotopes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     35      10    0.778  0.0620        0.665        0.909
   24     21      14    0.467  0.0744        0.341        0.638
   36      9       8    0.272  0.0687        0.165        0.446
   48      4       2    0.211  0.0653        0.115        0.387
   60      3       0    0.211  0.0653        0.115        0.387

                RX_SUMM_RADIATION_F=Radiation, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     49      34    0.591  0.0539        0.494        0.707
   24     33      11    0.454  0.0551        0.358        0.576
   36     19       9    0.325  0.0537        0.235        0.449
   48     15       2    0.288  0.0536        0.200        0.415
   60     12       2    0.248  0.0531        0.163        0.377
  120      3       0    0.248  0.0531        0.163        0.377

                RX_SUMM_RADIATION_F=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1555      80    0.953 0.00518        0.942        0.963
   24   1410      63    0.913 0.00697        0.899        0.927
   36   1287      49    0.880 0.00813        0.864        0.896
   48   1206      34    0.857 0.00887        0.839        0.874
   60   1093      49    0.821 0.00985        0.802        0.841
  120    368     131    0.682 0.01417        0.655        0.710




   
## Univariable Cox Proportional Hazard Model for:  RX_SUMM_RADIATION_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_SUMM_RADIATION_F, data = data)

  n= 269089, number of events= 58360 

                                              coef exp(coef) se(coef)       z Pr(>|z|)    
RX_SUMM_RADIATION_FBeam Radiation          2.01913   7.53176  0.01352 149.369  < 2e-16 ***
RX_SUMM_RADIATION_FRadioactive Implants    1.47544   4.37298  0.15437   9.558  < 2e-16 ***
RX_SUMM_RADIATION_FRadioisotopes           1.61628   5.03434  0.57737   2.799  0.00512 ** 
RX_SUMM_RADIATION_FBeam + Imp or Isotopes  1.94091   6.96510  0.16911  11.477  < 2e-16 ***
RX_SUMM_RADIATION_FRadiation, NOS          1.84128   6.30458  0.13139  14.014  < 2e-16 ***
RX_SUMM_RADIATION_FUnknown                -0.04298   0.95793  0.04849  -0.886  0.37546    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                          exp(coef) exp(-coef) lower .95 upper .95
RX_SUMM_RADIATION_FBeam Radiation            7.5318     0.1328    7.3348     7.734
RX_SUMM_RADIATION_FRadioactive Implants      4.3730     0.2287    3.2313     5.918
RX_SUMM_RADIATION_FRadioisotopes             5.0343     0.1986    1.6236    15.610
RX_SUMM_RADIATION_FBeam + Imp or Isotopes    6.9651     0.1436    5.0001     9.702
RX_SUMM_RADIATION_FRadiation, NOS            6.3046     0.1586    4.8732     8.156
RX_SUMM_RADIATION_FUnknown                   0.9579     1.0439    0.8711     1.053

Concordance= 0.562  (se = 0 )
Rsquare= 0.051   (max possible= 0.994 )
Likelihood ratio test= 13991  on 6 df,   p=0
Wald test            = 22652  on 6 df,   p=0
Score (logrank) test = 31385  on 6 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RX_SUMM_RADIATION_F

Lymphovascular Invasion

uni_var(test_var = "LYMPH_VASCULAR_INVASION_F", data_imp = data)
_________________________________________________
   
## LYMPH_VASCULAR_INVASION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ LYMPH_VASCULAR_INVASION_F, data = data)

   119720 observations deleted due to missingness 
                                                 n events median 0.95LCL 0.95UCL
LYMPH_VASCULAR_INVASION_F=Neg_LymphVasc_Inv 106669  13009   95.9   95.93      NA
LYMPH_VASCULAR_INVASION_F=Pos_LumphVasc_Inv   5852   2420   53.2   50.17    56.5
LYMPH_VASCULAR_INVASION_F=N_A                   34     16   17.4    7.49      NA
LYMPH_VASCULAR_INVASION_F=Unknown            36814   8703   95.5      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ LYMPH_VASCULAR_INVASION_F, data = data)

119720 observations deleted due to missingness 
                LYMPH_VASCULAR_INVASION_F=Neg_LymphVasc_Inv 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12  88628    2899    0.970 0.000544        0.969        0.971
   24  71808    3448    0.930 0.000856        0.928        0.931
   36  51872    2757    0.889 0.001118        0.887        0.891
   48  35456    1808    0.853 0.001363        0.850        0.855
   60  21585    1118    0.819 0.001637        0.816        0.823

                LYMPH_VASCULAR_INVASION_F=Pos_LumphVasc_Inv 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4405     917    0.835 0.00498        0.826        0.845
   24   3125     734    0.687 0.00645        0.674        0.700
   36   2066     394    0.591 0.00716        0.577        0.605
   48   1320     200    0.524 0.00777        0.509        0.539
   60    715     104    0.473 0.00850        0.456        0.490

                LYMPH_VASCULAR_INVASION_F=N_A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     12      15    0.522  0.0906        0.371        0.733
   24      3       1    0.464  0.0973        0.307        0.700

                LYMPH_VASCULAR_INVASION_F=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12  27742    4464    0.871 0.00180        0.868        0.875
   24  22598    1761    0.813 0.00215        0.809        0.817
   36  17286    1076    0.770 0.00241        0.765        0.775
   48  12607     638    0.738 0.00262        0.733        0.743
   60   8071     418    0.708 0.00290        0.703        0.714




   
## Univariable Cox Proportional Hazard Model for:  LYMPH_VASCULAR_INVASION_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ LYMPH_VASCULAR_INVASION_F, data = data)

  n= 149369, number of events= 24148 
   (119720 observations deleted due to missingness)

                                               coef exp(coef) se(coef)     z Pr(>|z|)    
LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv  1.40672   4.08253  0.02216 63.48   <2e-16 ***
LYMPH_VASCULAR_INVASION_FN_A                2.52862  12.53615  0.25023 10.11   <2e-16 ***
LYMPH_VASCULAR_INVASION_FUnknown            0.71042   2.03484  0.01385 51.28   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                           exp(coef) exp(-coef) lower .95 upper .95
LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv     4.083    0.24495     3.909     4.264
LYMPH_VASCULAR_INVASION_FN_A                  12.536    0.07977     7.677    20.472
LYMPH_VASCULAR_INVASION_FUnknown               2.035    0.49144     1.980     2.091

Concordance= 0.625  (se = 0.002 )
Rsquare= 0.031   (max possible= 0.975 )
Likelihood ratio test= 4739  on 3 df,   p=0
Wald test            = 5441  on 3 df,   p=0
Score (logrank) test = 6055  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  LYMPH_VASCULAR_INVASION_F

Endoscopic/Robotic

uni_var(test_var = "RX_HOSP_SURG_APPR_2010_F", data_imp = data)
_________________________________________________
   
## RX_HOSP_SURG_APPR_2010_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)

   119720 observations deleted due to missingness 
                                               n events median 0.95LCL 0.95UCL
RX_HOSP_SURG_APPR_2010_F=No_Surg           14021   6564   37.8    35.4    40.4
RX_HOSP_SURG_APPR_2010_F=Robot_Assist        117     21     NA    68.0      NA
RX_HOSP_SURG_APPR_2010_F=Robot_to_Open        28      2     NA      NA      NA
RX_HOSP_SURG_APPR_2010_F=Endo_Lap            356     88     NA    85.3      NA
RX_HOSP_SURG_APPR_2010_F=Endo_Lap_to_Open    131     24     NA    81.5      NA
RX_HOSP_SURG_APPR_2010_F=Open_Unknown     134691  17440   95.9    95.5      NA
RX_HOSP_SURG_APPR_2010_F=Unknown              25      9   16.0    11.2      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)

119720 observations deleted due to missingness 
                RX_HOSP_SURG_APPR_2010_F=No_Surg 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   8123    4416    0.667 0.00411        0.659        0.675
   24   5881    1156    0.566 0.00444        0.558        0.575
   36   3966     546    0.507 0.00465        0.498        0.516
   48   2617     260    0.468 0.00488        0.459        0.478
   60   1550     108    0.444 0.00516        0.434        0.454

                RX_HOSP_SURG_APPR_2010_F=Robot_Assist 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     96       6    0.944  0.0224        0.901        0.989
   24     76       5    0.889  0.0318        0.829        0.953
   36     61       6    0.813  0.0415        0.736        0.899
   48     39       2    0.785  0.0447        0.702        0.877
   60     17       0    0.785  0.0447        0.702        0.877

                RX_HOSP_SURG_APPR_2010_F=Robot_to_Open 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     22       1    0.957  0.0425        0.877            1
   24     15       0    0.957  0.0425        0.877            1
   36     12       0    0.957  0.0425        0.877            1
   48      8       0    0.957  0.0425        0.877            1
   60      2       1    0.797  0.1498        0.552            1

                RX_HOSP_SURG_APPR_2010_F=Endo_Lap 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    283      28    0.915  0.0154        0.885        0.945
   24    221      21    0.842  0.0209        0.802        0.884
   36    157      20    0.755  0.0263        0.705        0.808
   48     99      11    0.692  0.0302        0.635        0.754
   60     55       7    0.634  0.0348        0.570        0.706

                RX_HOSP_SURG_APPR_2010_F=Endo_Lap_to_Open 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    102       6    0.949  0.0204        0.909        0.990
   24     92       3    0.920  0.0257        0.871        0.972
   36     77       5    0.866  0.0336        0.803        0.935
   48     54       3    0.829  0.0384        0.757        0.908
   60     40       1    0.812  0.0414        0.734        0.897

                RX_HOSP_SURG_APPR_2010_F=Open_Unknown 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 112151    3832    0.969 0.000495        0.968        0.970
   24  91242    4756    0.925 0.000784        0.923        0.926
   36  66945    3650    0.883 0.001011        0.881        0.885
   48  46563    2370    0.847 0.001216        0.844        0.849
   60  28705    1523    0.813 0.001450        0.810        0.816

                RX_HOSP_SURG_APPR_2010_F=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     10       6    0.641   0.118        0.447        0.921
   24      7       3    0.449   0.124        0.261        0.773
   36      6       0    0.449   0.124        0.261        0.773
   48      3       0    0.449   0.124        0.261        0.773
   60      2       0    0.449   0.124        0.261        0.773




   
## Univariable Cox Proportional Hazard Model for:  RX_HOSP_SURG_APPR_2010_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)

  n= 149369, number of events= 24148 
   (119720 observations deleted due to missingness)

                                             coef exp(coef) se(coef)        z Pr(>|z|)    
RX_HOSP_SURG_APPR_2010_FRobot_Assist     -1.33308   0.26366  0.21857   -6.099 1.07e-09 ***
RX_HOSP_SURG_APPR_2010_FRobot_to_Open    -2.11286   0.12089  0.70722   -2.988  0.00281 ** 
RX_HOSP_SURG_APPR_2010_FEndo_Lap         -0.94607   0.38826  0.10732   -8.816  < 2e-16 ***
RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open -1.42817   0.23975  0.20451   -6.983 2.88e-12 ***
RX_HOSP_SURG_APPR_2010_FOpen_Unknown     -1.69257   0.18405  0.01452 -116.530  < 2e-16 ***
RX_HOSP_SURG_APPR_2010_FUnknown           0.10349   1.10903  0.33356    0.310  0.75637    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                         exp(coef) exp(-coef) lower .95 upper .95
RX_HOSP_SURG_APPR_2010_FRobot_Assist        0.2637     3.7927   0.17179    0.4047
RX_HOSP_SURG_APPR_2010_FRobot_to_Open       0.1209     8.2719   0.03023    0.4835
RX_HOSP_SURG_APPR_2010_FEndo_Lap            0.3883     2.5756   0.31462    0.4792
RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open    0.2397     4.1710   0.16058    0.3580
RX_HOSP_SURG_APPR_2010_FOpen_Unknown        0.1840     5.4334   0.17888    0.1894
RX_HOSP_SURG_APPR_2010_FUnknown             1.1090     0.9017   0.57678    2.1324

Concordance= 0.636  (se = 0.001 )
Rsquare= 0.065   (max possible= 0.975 )
Likelihood ratio test= 10100  on 6 df,   p=0
Wald test            = 13605  on 6 df,   p=0
Score (logrank) test = 17140  on 6 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RX_HOSP_SURG_APPR_2010_F

Surgery Radiation Sequence

uni_var(test_var = "SURG_RAD_SEQ", data_imp = data)
_________________________________________________
   
## SURG_RAD_SEQ
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ, data = data)

                                            n events median 0.95LCL 0.95UCL
SURG_RAD_SEQ=Surg Alone                249888  45721     NA      NA      NA
SURG_RAD_SEQ=Surg then Rad               4529   2765  30.00   28.22   31.70
SURG_RAD_SEQ=Rad Alone                   4187   3528   5.95    5.62    6.28
SURG_RAD_SEQ=No Treatment                8084   5458  11.10   10.48   11.83
SURG_RAD_SEQ=Other                       2299    813 154.78  140.91      NA
SURG_RAD_SEQ=Rad before and after Surg     15     11  11.10    8.51      NA
SURG_RAD_SEQ=Rad then Surg                 87     64  16.62   12.39   24.67

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ, data = data)

                SURG_RAD_SEQ=Surg Alone 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 215373    7061    0.970 0.000355        0.969        0.970
   24 186896    9505    0.925 0.000562        0.924        0.926
   36 155753    7918    0.883 0.000707        0.882        0.884
   48 129243    5795    0.848 0.000817        0.846        0.849
   60 105072    4297    0.817 0.000912        0.815        0.819
  120  25677    9900    0.695 0.001448        0.692        0.698

                SURG_RAD_SEQ=Surg then Rad 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3231    1145    0.743 0.00656        0.730        0.756
   24   2227     748    0.564 0.00758        0.549        0.579
   36   1574     410    0.454 0.00782        0.439        0.469
   48   1163     185    0.396 0.00789        0.381        0.412
   60    855     117    0.353 0.00800        0.337        0.369
  120    165     148    0.256 0.00959        0.238        0.275

                SURG_RAD_SEQ=Rad Alone 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1248    2815   0.3176 0.00729       0.3037        0.332
   24    699     444   0.1997 0.00639       0.1875        0.213
   36    423     151   0.1520 0.00594       0.1408        0.164
   48    302      58   0.1294 0.00576       0.1186        0.141
   60    222      27   0.1170 0.00568       0.1064        0.129
  120     33      28   0.0913 0.00654       0.0794        0.105

                SURG_RAD_SEQ=No Treatment 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3439    3940    0.485 0.00576        0.474        0.496
   24   2285     832    0.362 0.00567        0.351        0.373
   36   1649     309    0.309 0.00558        0.299        0.321
   48   1223     164    0.276 0.00556        0.265        0.287
   60    936      74    0.257 0.00559        0.247        0.268
  120    217     127    0.204 0.00633        0.192        0.217

                SURG_RAD_SEQ=Other 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1733     374    0.830 0.00804        0.814        0.845
   24   1533     103    0.779 0.00896        0.762        0.797
   36   1377      72    0.741 0.00957        0.723        0.760
   48   1277      42    0.718 0.00991        0.699        0.738
   60   1149      59    0.684 0.01038        0.664        0.705
  120    379     140    0.566 0.01279        0.542        0.592

                SURG_RAD_SEQ=Rad before and after Surg 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       9     0.40   0.126       0.2152        0.743
   24      5       0     0.40   0.126       0.2152        0.743
   36      3       2     0.24   0.116       0.0931        0.619
   48      3       0     0.24   0.116       0.0931        0.619
   60      3       0     0.24   0.116       0.0931        0.619
  120      3       0     0.24   0.116       0.0931        0.619

                SURG_RAD_SEQ=Rad then Surg 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     52      34    0.603  0.0530       0.5072        0.716
   24     31      18    0.387  0.0532       0.2954        0.506
   36     19       8    0.284  0.0501       0.2010        0.401
   48     17       0    0.284  0.0501       0.2010        0.401
   60     13       0    0.284  0.0501       0.2010        0.401
  120      3       4    0.173  0.0543       0.0931        0.320




   
## Univariable Cox Proportional Hazard Model for:  SURG_RAD_SEQ

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ, data = data)

  n= 269089, number of events= 58360 

                                          coef exp(coef) se(coef)       z Pr(>|z|)    
SURG_RAD_SEQSurg then Rad              1.64715   5.19217  0.01963  83.916  < 2e-16 ***
SURG_RAD_SEQRad Alone                  2.82423  16.84800  0.01775 159.143  < 2e-16 ***
SURG_RAD_SEQNo Treatment               2.20704   9.08882  0.01441 153.185  < 2e-16 ***
SURG_RAD_SEQOther                      0.61731   1.85394  0.03539  17.441  < 2e-16 ***
SURG_RAD_SEQRad before and after Surg  1.95441   7.05974  0.30156   6.481 9.11e-11 ***
SURG_RAD_SEQRad then Surg              2.10172   8.18025  0.12510  16.800  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                      exp(coef) exp(-coef) lower .95 upper .95
SURG_RAD_SEQSurg then Rad                 5.192    0.19260     4.996     5.396
SURG_RAD_SEQRad Alone                    16.848    0.05935    16.272    17.444
SURG_RAD_SEQNo Treatment                  9.089    0.11003     8.836     9.349
SURG_RAD_SEQOther                         1.854    0.53939     1.730     1.987
SURG_RAD_SEQRad before and after Surg     7.060    0.14165     3.909    12.749
SURG_RAD_SEQRad then Surg                 8.180    0.12225     6.401    10.453

Concordance= 0.623  (se = 0 )
Rsquare= 0.106   (max possible= 0.994 )
Likelihood ratio test= 30027  on 6 df,   p=0
Wald test            = 48241  on 6 df,   p=0
Score (logrank) test = 76988  on 6 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SURG_RAD_SEQ

Surgery Yes/No

uni_var(test_var = "SURGERY_YN", data_imp = data)
_________________________________________________
   
## SURGERY_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURGERY_YN, data = data)

                    n events median 0.95LCL 0.95UCL
SURGERY_YN=No   12368   9042   8.41    8.05    8.74
SURGERY_YN=Ukn    510    336  13.96   11.24   16.59
SURGERY_YN=Yes 256211  48982     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURGERY_YN, data = data)

                SURGERY_YN=No 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4708    6815    0.425 0.00457        0.416        0.434
   24   3008    1271    0.305 0.00435        0.296        0.313
   36   2093     459    0.254 0.00423        0.246        0.263
   48   1549     220    0.225 0.00418        0.217        0.234
   60   1179     101    0.209 0.00418        0.201        0.218
  120    256     159    0.165 0.00470        0.156        0.175

                SURGERY_YN=Ukn 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    228     218    0.530  0.0233        0.486        0.577
   24    154      57    0.393  0.0233        0.350        0.441
   36    111      29    0.314  0.0228        0.273        0.362
   48     89       9    0.287  0.0226        0.246        0.335
   60     70      12    0.246  0.0222        0.207        0.294
  120     15      11    0.195  0.0225        0.156        0.245

                SURGERY_YN=Yes 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 220146    8345    0.965 0.000375        0.964        0.966
   24 190514   10322    0.918 0.000578        0.917        0.919
   36 158594    8382    0.875 0.000718        0.873        0.876
   48 131590    6015    0.839 0.000823        0.838        0.841
   60 107001    4461    0.808 0.000913        0.806        0.810
  120  26206   10177    0.687 0.001431        0.684        0.689




   
## Univariable Cox Proportional Hazard Model for:  SURGERY_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURGERY_YN, data = data)

  n= 269089, number of events= 58360 

                  coef exp(coef) se(coef)        z Pr(>|z|)    
SURGERY_YNUkn -0.30186   0.73944  0.05556   -5.433 5.55e-08 ***
SURGERY_YNYes -2.34789   0.09557  0.01161 -202.229  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
SURGERY_YNUkn   0.73944      1.352   0.66315   0.82452
SURGERY_YNYes   0.09557     10.463   0.09342   0.09777

Concordance= 0.599  (se = 0 )
Rsquare= 0.09   (max possible= 0.994 )
Likelihood ratio test= 25267  on 2 df,   p=0
Wald test            = 41800  on 2 df,   p=0
Score (logrank) test = 64329  on 2 df,   p=0
no non-missing arguments to min; returning Infno non-missing arguments to max; returning -InfTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 1 rows containing missing values (geom_errorbar).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SURGERY_YN

Radiation Yes/No

uni_var(test_var = "RADIATION_YN", data_imp = data)
_________________________________________________
   
## RADIATION_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RADIATION_YN, data = data)

   1913 observations deleted due to missingness 
                      n events median 0.95LCL 0.95UCL
RADIATION_YN=No  258334  51411     NA      NA      NA
RADIATION_YN=Yes   8842   6382   14.4    13.7    15.1

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RADIATION_YN, data = data)

1913 observations deleted due to missingness 
                RADIATION_YN=No 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 218976   11150    0.954 0.000424        0.953        0.955
   24 189294   10376    0.907 0.000606        0.906        0.908
   36 157485    8249    0.865 0.000735        0.863        0.866
   48 130533    5965    0.830 0.000835        0.828        0.831
   60 106060    4381    0.799 0.000921        0.798        0.801
  120  25905   10033    0.680 0.001426        0.677        0.682

                RADIATION_YN=Yes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   4549    4011    0.540 0.00535        0.530        0.551
   24   2971    1211    0.390 0.00533        0.380        0.401
   36   2026     571    0.310 0.00519        0.300        0.320
   48   1489     245    0.270 0.00512        0.260        0.280
   60   1097     144    0.241 0.00510        0.231        0.251
  120    204     183    0.177 0.00592        0.166        0.189




   
## Univariable Cox Proportional Hazard Model for:  RADIATION_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RADIATION_YN, data = data)

  n= 267176, number of events= 57793 
   (1913 observations deleted due to missingness)

                   coef exp(coef) se(coef)     z Pr(>|z|)    
RADIATION_YNYes 2.01638   7.51106  0.01339 150.6   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
RADIATION_YNYes     7.511     0.1331     7.316     7.711

Concordance= 0.562  (se = 0 )
Rsquare= 0.051   (max possible= 0.994 )
Likelihood ratio test= 14012  on 1 df,   p=0
Wald test            = 22675  on 1 df,   p=0
Score (logrank) test = 31419  on 1 df,   p=0





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RADIATION_YN

Chemo Yes/No

uni_var(test_var = "CHEMO_YN", data_imp = data)
_________________________________________________
   
## CHEMO_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CHEMO_YN, data = data)

                  n events median 0.95LCL 0.95UCL
CHEMO_YN=No  253329  51407     NA      NA      NA
CHEMO_YN=Yes   7241   5006   16.1    15.4    16.9
CHEMO_YN=Ukn   8519   1947     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CHEMO_YN, data = data)

                CHEMO_YN=No 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 213928   11856    0.951 0.000443        0.950        0.951
   24 184701   10208    0.903 0.000622        0.902        0.904
   36 153351    8154    0.860 0.000751        0.859        0.862
   48 126949    5852    0.825 0.000851        0.824        0.827
   60 103015    4301    0.795 0.000937        0.793        0.797
  120  24943    9811    0.674 0.001449        0.672        0.677

                CHEMO_YN=Yes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   3931    3026    0.574 0.00589        0.562        0.585
   24   2604    1084    0.410 0.00595        0.399        0.422
   36   1940     422    0.341 0.00583        0.330        0.352
   48   1522     202    0.303 0.00576        0.292        0.315
   60   1197      98    0.282 0.00574        0.271        0.294
  120    298     158    0.229 0.00618        0.218        0.242

                CHEMO_YN=Ukn 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   7223     496    0.939 0.00266        0.934        0.944
   24   6371     358    0.891 0.00354        0.884        0.898
   36   5507     294    0.848 0.00417        0.839        0.856
   48   4757     190    0.817 0.00458        0.808        0.826
   60   4038     175    0.785 0.00500        0.775        0.795
  120   1236     378    0.677 0.00695        0.663        0.691




   
## Univariable Cox Proportional Hazard Model for:  CHEMO_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CHEMO_YN, data = data)

  n= 269089, number of events= 58360 

               coef exp(coef) se(coef)      z Pr(>|z|)    
CHEMO_YNYes 1.80502   6.08011  0.01487 121.40   <2e-16 ***
CHEMO_YNUkn 0.02886   1.02928  0.02309   1.25    0.211    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

            exp(coef) exp(-coef) lower .95 upper .95
CHEMO_YNYes     6.080     0.1645    5.9055     6.260
CHEMO_YNUkn     1.029     0.9715    0.9837     1.077

Concordance= 0.547  (se = 0 )
Rsquare= 0.034   (max possible= 0.994 )
Likelihood ratio test= 9353  on 2 df,   p=0
Wald test            = 14768  on 2 df,   p=0
Score (logrank) test = 19221  on 2 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  CHEMO_YN

Treatment Yes/No

uni_var(test_var = "Tx_YN", data_imp = data)
_________________________________________________
   
## Tx_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ Tx_YN, data = data)

   8519 observations deleted due to missingness 
                 n events median 0.95LCL 0.95UCL
Tx_YN=FALSE   6106   3952     12    10.9    12.9
Tx_YN=TRUE  254464  52461     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ Tx_YN, data = data)

8519 observations deleted due to missingness 
                Tx_YN=FALSE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   2646    2887    0.499 0.00663        0.487        0.513
   24   1857     529    0.395 0.00663        0.382        0.408
   36   1355     230    0.342 0.00660        0.329        0.355
   48   1013     124    0.308 0.00662        0.295        0.321
   60    782      59    0.288 0.00668        0.275        0.301
  120    171     114    0.223 0.00775        0.208        0.239

                Tx_YN=TRUE 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 215213   11995    0.950 0.000446        0.949        0.951
   24 185448   10763    0.900 0.000629        0.899        0.902
   36 153936    8346    0.857 0.000757        0.856        0.858
   48 127458    5930    0.822 0.000855        0.820        0.823
   60 103430    4340    0.791 0.000940        0.789        0.793
  120  25070    9855    0.671 0.001442        0.669        0.674




   
## Univariable Cox Proportional Hazard Model for:  Tx_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ Tx_YN, data = data)

  n= 260570, number of events= 56413 
   (8519 observations deleted due to missingness)

              coef exp(coef) se(coef)      z Pr(>|z|)    
Tx_YNTRUE -1.96628   0.13998  0.01655 -118.8   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

          exp(coef) exp(-coef) lower .95 upper .95
Tx_YNTRUE      0.14      7.144    0.1355    0.1446

Concordance= 0.542  (se = 0 )
Rsquare= 0.032   (max possible= 0.994 )
Likelihood ratio test= 8508  on 1 df,   p=0
Wald test            = 14109  on 1 df,   p=0
Score (logrank) test = 19273  on 1 df,   p=0





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  Tx_YN

Metastases at Dx

uni_var(test_var = "mets_at_dx_F", data_imp = data)
_________________________________________________
   
## mets_at_dx_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ mets_at_dx_F, data = data)

                        n events median 0.95LCL 0.95UCL
mets_at_dx_F=FALSE 264940  54910     NA      NA      NA
mets_at_dx_F=TRUE    4149   3450   5.52    5.22    5.75

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ mets_at_dx_F, data = data)

                mets_at_dx_F=FALSE 
 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
   12 223970   12579    0.950 0.000438        0.949        0.951
   24 193145   11202    0.900 0.000617        0.899        0.901
   36 160542    8729    0.857 0.000742        0.855        0.858
   48 133098    6206    0.821 0.000838        0.819        0.823
   60 108193    4558    0.791 0.000921        0.789        0.792
  120  26477   10339    0.671 0.001408        0.668        0.674

                mets_at_dx_F=TRUE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1112    2799   0.3007 0.00732       0.2867        0.315
   24    531     448   0.1724 0.00625       0.1606        0.185
   36    256     141   0.1186 0.00574       0.1079        0.130
   48    130      38   0.0974 0.00569       0.0869        0.109
   60     57      16   0.0812 0.00607       0.0701        0.094




   
## Univariable Cox Proportional Hazard Model for:  mets_at_dx_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ mets_at_dx_F, data = data)

  n= 269089, number of events= 58360 

                     coef exp(coef) se(coef)     z Pr(>|z|)    
mets_at_dx_FTRUE  2.89601  18.10177  0.01807 160.3   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                 exp(coef) exp(-coef) lower .95 upper .95
mets_at_dx_FTRUE      18.1    0.05524     17.47     18.75

Concordance= 0.539  (se = 0 )
Rsquare= 0.047   (max possible= 0.994 )
Likelihood ratio test= 13084  on 1 df,   p=0
Wald test            = 25698  on 1 df,   p=0
Score (logrank) test = 49303  on 1 df,   p=0





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  mets_at_dx_F

Tumor specific Variables

Node Size

Cox Proportional Hazard Ratio

Model #1

Full analysis

model_one <- coxph(Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)
                     ~ SURG_RAD_SEQ + INSURANCE_F + AGE + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F,
                     data = data)
X matrix deemed to be singular; variable 11
model_one %>% summary()
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ + INSURANCE_F + AGE + SEX_F + RACE_F + 
    INCOME_F + U_R_F + FACILITY_TYPE_F + FACILITY_LOCATION_F + 
    EDUCATION_F, data = data)

  n= 223688, number of events= 53890 
   (45401 observations deleted due to missingness)

                                                   coef  exp(coef)   se(coef)       z Pr(>|z|)    
SURG_RAD_SEQSurg then Rad                     1.3334890  3.7942584  0.0207881  64.147  < 2e-16 ***
SURG_RAD_SEQRad Alone                         2.6257681 13.8151819  0.0187879 139.758  < 2e-16 ***
SURG_RAD_SEQNo Treatment                      2.0220291  7.5536363  0.0151774 133.226  < 2e-16 ***
SURG_RAD_SEQOther                             0.5727957  1.7732175  0.0368887  15.528  < 2e-16 ***
SURG_RAD_SEQRad before and after Surg         2.4639754 11.7514357  0.3334653   7.389 1.48e-13 ***
SURG_RAD_SEQRad then Surg                     1.7519354  5.7657508  0.1304128  13.434  < 2e-16 ***
INSURANCE_FNone                               0.8209855  2.2727384  0.0258866  31.715  < 2e-16 ***
INSURANCE_FMedicaid                           1.0004722  2.7195656  0.0247808  40.373  < 2e-16 ***
INSURANCE_FMedicare                           0.1935083  1.2134995  0.0124053  15.599  < 2e-16 ***
INSURANCE_FOther Government                   0.2588749  1.2954717  0.0441113   5.869 4.39e-09 ***
INSURANCE_FUnknown                                   NA         NA  0.0000000      NA       NA    
AGE                                           0.0539653  1.0554480  0.0004743 113.786  < 2e-16 ***
SEX_FFemale                                  -0.3085452  0.7345147  0.0090604 -34.054  < 2e-16 ***
RACE_FBlack                                   0.3306533  1.3918772  0.0383802   8.615  < 2e-16 ***
RACE_FOther/Unk                              -0.0729303  0.9296657  0.0345090  -2.113 0.034569 *  
RACE_FAsian                                   0.1298987  1.1387131  0.0667022   1.947 0.051482 .  
INCOME_F$38,000 - $47,999                    -0.0294684  0.9709616  0.0152906  -1.927 0.053952 .  
INCOME_F$48,000 - $62,999                    -0.0823765  0.9209252  0.0163096  -5.051 4.40e-07 ***
INCOME_F$63,000 +                            -0.1521152  0.8588893  0.0185055  -8.220 2.22e-16 ***
U_R_FUrban                                   -0.0386369  0.9620999  0.0130657  -2.957 0.003105 ** 
U_R_FRural                                   -0.0275118  0.9728632  0.0306390  -0.898 0.369221    
FACILITY_TYPE_FComprehensive Comm Ca Program -0.0398850  0.9608999  0.0169910  -2.347 0.018904 *  
FACILITY_TYPE_FAcademic/Research Program     -0.1218022  0.8853235  0.0169484  -7.187 6.64e-13 ***
FACILITY_TYPE_FIntegrated Network Ca Program  0.0046987  1.0047097  0.0200303   0.235 0.814537    
FACILITY_LOCATION_FMiddle Atlantic           -0.0062098  0.9938094  0.0216611  -0.287 0.774357    
FACILITY_LOCATION_FSouth Atlantic            -0.0135671  0.9865246  0.0208953  -0.649 0.516151    
FACILITY_LOCATION_FEast North Central         0.0890028  1.0930837  0.0213804   4.163 3.14e-05 ***
FACILITY_LOCATION_FEast South Central         0.0774842  1.0805652  0.0248013   3.124 0.001783 ** 
FACILITY_LOCATION_FWest North Central         0.0502383  1.0515216  0.0244411   2.055 0.039833 *  
FACILITY_LOCATION_FWest South Central         0.0131713  1.0132584  0.0258735   0.509 0.610708    
FACILITY_LOCATION_FMountain                   0.0366711  1.0373518  0.0271386   1.351 0.176615    
FACILITY_LOCATION_FPacific                   -0.0309207  0.9695525  0.0226947  -1.362 0.173052    
EDUCATION_F13 - 20.9%                        -0.0573047  0.9443063  0.0153599  -3.731 0.000191 ***
EDUCATION_F7 - 12.9%                         -0.1072284  0.8983205  0.0163720  -6.550 5.77e-11 ***
EDUCATION_FLess than 7%                      -0.2495956  0.7791158  0.0189037 -13.204  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                             exp(coef) exp(-coef) lower .95 upper .95
SURG_RAD_SEQSurg then Rad                       3.7943    0.26356    3.6428    3.9520
SURG_RAD_SEQRad Alone                          13.8152    0.07238   13.3157   14.3334
SURG_RAD_SEQNo Treatment                        7.5536    0.13239    7.3322    7.7817
SURG_RAD_SEQOther                               1.7732    0.56395    1.6495    1.9062
SURG_RAD_SEQRad before and after Surg          11.7514    0.08510    6.1129   22.5911
SURG_RAD_SEQRad then Surg                       5.7658    0.17344    4.4653    7.4450
INSURANCE_FNone                                 2.2727    0.44000    2.1603    2.3910
INSURANCE_FMedicaid                             2.7196    0.36771    2.5906    2.8549
INSURANCE_FMedicare                             1.2135    0.82406    1.1844    1.2434
INSURANCE_FOther Government                     1.2955    0.77192    1.1882    1.4125
INSURANCE_FUnknown                                  NA         NA        NA        NA
AGE                                             1.0554    0.94746    1.0545    1.0564
SEX_FFemale                                     0.7345    1.36144    0.7216    0.7477
RACE_FBlack                                     1.3919    0.71845    1.2910    1.5006
RACE_FOther/Unk                                 0.9297    1.07566    0.8689    0.9947
RACE_FAsian                                     1.1387    0.87818    0.9992    1.2978
INCOME_F$38,000 - $47,999                       0.9710    1.02991    0.9423    1.0005
INCOME_F$48,000 - $62,999                       0.9209    1.08586    0.8920    0.9508
INCOME_F$63,000 +                               0.8589    1.16429    0.8283    0.8906
U_R_FUrban                                      0.9621    1.03939    0.9378    0.9871
U_R_FRural                                      0.9729    1.02789    0.9162    1.0331
FACILITY_TYPE_FComprehensive Comm Ca Program    0.9609    1.04069    0.9294    0.9934
FACILITY_TYPE_FAcademic/Research Program        0.8853    1.12953    0.8564    0.9152
FACILITY_TYPE_FIntegrated Network Ca Program    1.0047    0.99531    0.9660    1.0449
FACILITY_LOCATION_FMiddle Atlantic              0.9938    1.00623    0.9525    1.0369
FACILITY_LOCATION_FSouth Atlantic               0.9865    1.01366    0.9469    1.0278
FACILITY_LOCATION_FEast North Central           1.0931    0.91484    1.0482    1.1399
FACILITY_LOCATION_FEast South Central           1.0806    0.92544    1.0293    1.1344
FACILITY_LOCATION_FWest North Central           1.0515    0.95100    1.0023    1.1031
FACILITY_LOCATION_FWest South Central           1.0133    0.98692    0.9632    1.0660
FACILITY_LOCATION_FMountain                     1.0374    0.96399    0.9836    1.0940
FACILITY_LOCATION_FPacific                      0.9696    1.03140    0.9274    1.0137
EDUCATION_F13 - 20.9%                           0.9443    1.05898    0.9163    0.9732
EDUCATION_F7 - 12.9%                            0.8983    1.11319    0.8700    0.9276
EDUCATION_FLess than 7%                         0.7791    1.28351    0.7508    0.8085

Concordance= 0.78  (se = 0.001 )
Rsquare= 0.227   (max possible= 0.996 )
Likelihood ratio test= 57605  on 34 df,   p=0
Wald test            = 68966  on 34 df,   p=0
Score (logrank) test = 95593  on 34 df,   p=0

Summary of Model

model_one %>%
        tidy(., exponentiate = TRUE) %>%
        select(term, estimate, conf.low, conf.high, p.value) %>%
        rename(Variable = term,
               Hazard_Ratio = estimate) %>%
        tbl_df %>%
        print(n = nrow(.))

Prediction Logistic Regression Models

Surgery

no_Ukns <- data %>%
  filter(SURGERY_YN != "Ukn") %>% 
  droplevels() %>% 
  mutate(SURGERY_YN = as.logical(SURGERY_YN))
fit_surg <- glm(SURG_TF ~ AGE_F + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP,
   data = no_Ukns)
summary(fit_surg)

Call:
glm(formula = SURG_TF ~ AGE_F + SEX_F + RACE_F + INCOME_F + U_R_F + 
    FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP, 
    data = no_Ukns)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-0.99621   0.03177   0.04624   0.06075   0.23517  

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                   9.064e-01  3.406e-03 266.120  < 2e-16 ***
AGE_F(54,64]                                 -8.227e-03  1.226e-03  -6.711 1.93e-11 ***
AGE_F(64,74]                                 -1.053e-02  1.291e-03  -8.160 3.37e-16 ***
AGE_F(74,100]                                -2.423e-02  1.302e-03 -18.613  < 2e-16 ***
SEX_FFemale                                   1.387e-02  9.323e-04  14.880  < 2e-16 ***
RACE_FBlack                                  -1.042e-01  5.556e-03 -18.750  < 2e-16 ***
RACE_FOther/Unk                              -2.474e-03  3.441e-03  -0.719  0.47220    
RACE_FAsian                                  -7.105e-02  8.227e-03  -8.636  < 2e-16 ***
INCOME_F$38,000 - $47,999                     4.545e-03  1.811e-03   2.510  0.01206 *  
INCOME_F$48,000 - $62,999                     9.487e-03  1.896e-03   5.003 5.64e-07 ***
INCOME_F$63,000 +                             1.235e-02  2.086e-03   5.917 3.28e-09 ***
U_R_FUrban                                    5.802e-03  1.448e-03   4.007 6.15e-05 ***
U_R_FRural                                   -3.543e-05  3.525e-03  -0.010  0.99198    
FACILITY_TYPE_FComprehensive Comm Ca Program  2.356e-02  1.971e-03  11.953  < 2e-16 ***
FACILITY_TYPE_FAcademic/Research Program      3.881e-02  1.941e-03  20.001  < 2e-16 ***
FACILITY_TYPE_FIntegrated Network Ca Program  2.502e-02  2.272e-03  11.010  < 2e-16 ***
FACILITY_LOCATION_FMiddle Atlantic           -3.576e-03  2.233e-03  -1.602  0.10926    
FACILITY_LOCATION_FSouth Atlantic            -5.746e-03  2.202e-03  -2.609  0.00907 ** 
FACILITY_LOCATION_FEast North Central        -5.163e-03  2.236e-03  -2.309  0.02093 *  
FACILITY_LOCATION_FEast South Central        -3.720e-03  2.688e-03  -1.384  0.16642    
FACILITY_LOCATION_FWest North Central        -1.009e-02  2.510e-03  -4.020 5.82e-05 ***
FACILITY_LOCATION_FWest South Central        -2.529e-02  2.816e-03  -8.981  < 2e-16 ***
FACILITY_LOCATION_FMountain                  -1.262e-02  2.824e-03  -4.468 7.89e-06 ***
FACILITY_LOCATION_FPacific                   -1.297e-02  2.374e-03  -5.463 4.70e-08 ***
EDUCATION_F13 - 20.9%                         8.091e-03  1.812e-03   4.466 7.98e-06 ***
EDUCATION_F7 - 12.9%                          1.294e-02  1.896e-03   6.823 8.92e-12 ***
EDUCATION_FLess than 7%                       1.846e-02  2.107e-03   8.762  < 2e-16 ***
EXPN_GROUPPre-Expansion                       6.361e-03  1.331e-03   4.778 1.77e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.04678046)

    Null deviance: 10550  on 223226  degrees of freedom
Residual deviance: 10441  on 223199  degrees of freedom
  (45352 observations deleted due to missingness)
AIC: -50065

Number of Fisher Scoring iterations: 2
exp(cbind("Odds ratio" = coef(fit_surg), confint.default(fit_surg, level = 0.95)))
                                             Odds ratio     2.5 %    97.5 %
(Intercept)                                   2.4752849 2.4588167 2.4918634
AGE_F(54,64]                                  0.9918070 0.9894270 0.9941927
AGE_F(64,74]                                  0.9895242 0.9870243 0.9920304
AGE_F(74,100]                                 0.9760624 0.9735754 0.9785558
SEX_FFemale                                   1.0139692 1.0121182 1.0158237
RACE_FBlack                                   0.9010737 0.8913151 0.9109392
RACE_FOther/Unk                               0.9975292 0.9908240 1.0042798
RACE_FAsian                                   0.9314155 0.9165171 0.9465560
INCOME_F$38,000 - $47,999                     1.0045557 1.0009972 1.0081268
INCOME_F$48,000 - $62,999                     1.0095318 1.0057870 1.0132906
INCOME_F$63,000 +                             1.0124221 1.0082904 1.0165707
U_R_FUrban                                    1.0058191 1.0029685 1.0086778
U_R_FRural                                    0.9999646 0.9930806 1.0068963
FACILITY_TYPE_FComprehensive Comm Ca Program  1.0238422 1.0198942 1.0278055
FACILITY_TYPE_FAcademic/Research Program      1.0395763 1.0356298 1.0435378
FACILITY_TYPE_FIntegrated Network Ca Program  1.0253326 1.0207765 1.0299091
FACILITY_LOCATION_FMiddle Atlantic            0.9964306 0.9920797 1.0008006
FACILITY_LOCATION_FSouth Atlantic             0.9942702 0.9899881 0.9985707
FACILITY_LOCATION_FEast North Central         0.9948500 0.9904999 0.9992192
FACILITY_LOCATION_FEast South Central         0.9962868 0.9910511 1.0015502
FACILITY_LOCATION_FWest North Central         0.9899593 0.9851006 0.9948420
FACILITY_LOCATION_FWest South Central         0.9750278 0.9696613 0.9804239
FACILITY_LOCATION_FMountain                   0.9874597 0.9820087 0.9929409
FACILITY_LOCATION_FPacific                    0.9871163 0.9825342 0.9917197
EDUCATION_F13 - 20.9%                         1.0081233 1.0045500 1.0117094
EDUCATION_F7 - 12.9%                          1.0130203 1.0092631 1.0167916
EDUCATION_FLess than 7%                       1.0186333 1.0144354 1.0228485
EXPN_GROUPPre-Expansion                       1.0063815 1.0037589 1.0090108

Metastasis at Time of Diagnosis

# limit to those cases where data about expansion status is available (> Age 39, non-ambiguous status states)
fit_mets <- glm(mets_at_dx_F ~ AGE_F + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP,
   data = no_Excludes)
summary(fit_mets)

Call:
glm(formula = mets_at_dx_F ~ AGE_F + SEX_F + RACE_F + INCOME_F + 
    U_R_F + FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + 
    EXPN_GROUP, data = no_Excludes)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-0.08040  -0.02911  -0.02070  -0.01310   1.00441  

Coefficients:
                                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                   0.0512077  0.0022525  22.734  < 2e-16 ***
AGE_F(54,64]                                  0.0053229  0.0008100   6.572 4.98e-11 ***
AGE_F(64,74]                                  0.0057667  0.0008536   6.756 1.42e-11 ***
AGE_F(74,100]                                 0.0082184  0.0008624   9.530  < 2e-16 ***
SEX_FFemale                                  -0.0104857  0.0006175 -16.981  < 2e-16 ***
RACE_FBlack                                   0.0204356  0.0036294   5.631 1.80e-08 ***
RACE_FOther/Unk                              -0.0017112  0.0022829  -0.750 0.453511    
RACE_FAsian                                   0.0206698  0.0054289   3.807 0.000140 ***
INCOME_F$38,000 - $47,999                     0.0001936  0.0011931   0.162 0.871116    
INCOME_F$48,000 - $62,999                    -0.0022518  0.0012502  -1.801 0.071677 .  
INCOME_F$63,000 +                            -0.0064921  0.0013771  -4.714 2.43e-06 ***
U_R_FUrban                                   -0.0017842  0.0009560  -1.866 0.062006 .  
U_R_FRural                                    0.0030800  0.0023324   1.321 0.186656    
FACILITY_TYPE_FComprehensive Comm Ca Program -0.0058752  0.0013063  -4.497 6.88e-06 ***
FACILITY_TYPE_FAcademic/Research Program     -0.0095251  0.0012858  -7.408 1.29e-13 ***
FACILITY_TYPE_FIntegrated Network Ca Program -0.0009967  0.0015018  -0.664 0.506900    
FACILITY_LOCATION_FMiddle Atlantic            0.0013013  0.0014803   0.879 0.379356    
FACILITY_LOCATION_FSouth Atlantic             0.0046441  0.0014584   3.184 0.001451 ** 
FACILITY_LOCATION_FEast North Central         0.0043075  0.0014820   2.907 0.003654 ** 
FACILITY_LOCATION_FEast South Central         0.0042611  0.0017737   2.402 0.016286 *  
FACILITY_LOCATION_FWest North Central         0.0017214  0.0016638   1.035 0.300829    
FACILITY_LOCATION_FWest South Central         0.0137272  0.0018600   7.380 1.58e-13 ***
FACILITY_LOCATION_FMountain                   0.0077611  0.0018666   4.158 3.21e-05 ***
FACILITY_LOCATION_FPacific                   -0.0001001  0.0015739  -0.064 0.949290    
EDUCATION_F13 - 20.9%                        -0.0016303  0.0011931  -1.366 0.171793    
EDUCATION_F7 - 12.9%                         -0.0045653  0.0012497  -3.653 0.000259 ***
EDUCATION_FLess than 7%                      -0.0060403  0.0013904  -4.344 1.40e-05 ***
EXPN_GROUPPre-Expansion                      -0.0228379  0.0008846 -25.817  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.02148352)

    Null deviance: 5063.5  on 234201  degrees of freedom
Residual deviance: 5030.9  on 234174  degrees of freedom
  (7825 observations deleted due to missingness)
AIC: -234779

Number of Fisher Scoring iterations: 2
exp(cbind("Odds ratio" = coef(fit_mets), confint.default(fit_surg, level = 0.95)))
                                             Odds ratio     2.5 %    97.5 %
(Intercept)                                   1.0525415 2.4588167 2.4918634
AGE_F(54,64]                                  1.0053371 0.9894270 0.9941927
AGE_F(64,74]                                  1.0057834 0.9870243 0.9920304
AGE_F(74,100]                                 1.0082523 0.9735754 0.9785558
SEX_FFemale                                   0.9895691 1.0121182 1.0158237
RACE_FBlack                                   1.0206459 0.8913151 0.9109392
RACE_FOther/Unk                               0.9982903 0.9908240 1.0042798
RACE_FAsian                                   1.0208849 0.9165171 0.9465560
INCOME_F$38,000 - $47,999                     1.0001936 1.0009972 1.0081268
INCOME_F$48,000 - $62,999                     0.9977507 1.0057870 1.0132906
INCOME_F$63,000 +                             0.9935289 1.0082904 1.0165707
U_R_FUrban                                    0.9982174 1.0029685 1.0086778
U_R_FRural                                    1.0030848 0.9930806 1.0068963
FACILITY_TYPE_FComprehensive Comm Ca Program  0.9941420 1.0198942 1.0278055
FACILITY_TYPE_FAcademic/Research Program      0.9905201 1.0356298 1.0435378
FACILITY_TYPE_FIntegrated Network Ca Program  0.9990038 1.0207765 1.0299091
FACILITY_LOCATION_FMiddle Atlantic            1.0013022 0.9920797 1.0008006
FACILITY_LOCATION_FSouth Atlantic             1.0046549 0.9899881 0.9985707
FACILITY_LOCATION_FEast North Central         1.0043168 0.9904999 0.9992192
FACILITY_LOCATION_FEast South Central         1.0042702 0.9910511 1.0015502
FACILITY_LOCATION_FWest North Central         1.0017229 0.9851006 0.9948420
FACILITY_LOCATION_FWest South Central         1.0138219 0.9696613 0.9804239
FACILITY_LOCATION_FMountain                   1.0077913 0.9820087 0.9929409
FACILITY_LOCATION_FPacific                    0.9998999 0.9825342 0.9917197
EDUCATION_F13 - 20.9%                         0.9983710 1.0045500 1.0117094
EDUCATION_F7 - 12.9%                          0.9954451 1.0092631 1.0167916
EDUCATION_FLess than 7%                       0.9939779 1.0144354 1.0228485
EXPN_GROUPPre-Expansion                       0.9774209 1.0037589 1.0090108
---
title: "Melanoma ACA Analysis - A Review of the NCDB"
author: "Ramie Fathy"
date: "10/24/2019"
output:
  html_notebook:
    theme: united
    toc: yes
    toc_float: yes
  html_document:
    toc: yes
---
 


```{r chunk1, echo=FALSE, warning=FALSE, message=FALSE}

# questions: 
##  do we want to include patients <18yo?

library("ggplot2")
library("dplyr")
library("tidyr")
library("knitr")
library("tableone")
library("forcats")
library("survival")
library("npsurv")
library("broom")
library("tibble")
library("readr")
library("survminer")
library("stringr")

knitr::opts_chunk$set(echo=TRUE, warning=FALSE, message=TRUE)
'%!in%' <- function(x,y)!('%in%'(x,y))
```

```{r}
p_table <- function(tab_data, ...) {
  tab_data_2 <- deparse(substitute(tab_data))
  
  table_p <- do.call(CreateTableOne, 
                     list(data = as.name(tab_data_2), includeNA = TRUE, ...))
  table_p_out <- print(table_p,
                       showAllLevels = TRUE,
                       printToggle = FALSE)
  kable(table_p_out,
        align = "c")
}
```

```{r}
uni_var <- function(test_var, data_imp) {

                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")

        
        f <- as.formula(paste("Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)",
                              as.name(test_var),
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))

        km_fit <- do.call("survfit", list(formula = f, data = as.name(data_imp_2)))

        print(km_fit)
        cat("\n")

        print(summary(km_fit, times = c(12, 24, 36, 48, 60, 120)))
        cat("\n")


        cat("\n")
        cat("\n")
        cat("   \n## Univariable Cox Proportional Hazard Model for: ", test_var)
        cat("\n")
        cat("\n")


        n_levels <- nlevels(data_imp[[test_var]])

        if(n_levels == 1){
                print("Only one level, no Cox model performed")
                cat("\n")

        } else {


                cox_fit <- do.call("coxph", list(formula = f, data = as.name(data_imp_2)))

                print(summary(cox_fit))
                cat("\n")
                
                do.call("ggforest",
                         list(model = cox_fit, data = as.name(data_imp_2)))


        }

        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Unadjusted Kaplan Meier Overall Survival Curve for: ", test_var)


        p <- do.call("ggsurvplot",
                     list(fit = km_fit, data = as.name(data_imp_2),
                          palette = "jco", censor = FALSE, legend = "right",
                          linetype = "strata", xlab = "Time (Months)"))

        print(p)

}

```


```{r chunk2, cache=TRUE, message=FALSE, warning=FALSE, results='hide'}
col.width <- c(37, 10, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 4, 4, 1, 4, 1, 1,
               1, 3, 2, 2, 8, 2, 5, 5, 5, 4, 5, 5, 5,4, 2, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 3,
               3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 8,
               8, 8, 2, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 2, 2, 5, 2, 5, 3, 1, 3, 1, 8, 8, 2, 8,
               2, 8, 2, 2, 1, 8, 1, 1, 1, 1, 1, 8, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 3, 1, 1,
               1, 1, 1, 1, 1, 1, 1)

col.names.abr <- c("PUF_CASE_ID", "PUF_FACILITY_ID", "FACILITY_TYPE_CD", "FACILITY_LOCATION_CD",
                   "AGE", "SEX", "RACE", "SPANISH_HISPANIC_ORIGIN", "INSURANCE_STATUS",
                   "MED_INC_QUAR_00", "NO_HSD_QUAR_00", "UR_CD_03", "MED_INC_QUAR_12", "NO_HSD_QUAR_12",
                   "UR_CD_13", "CROWFLY", "CDCC_TOTAL_BEST", "SEQUENCE_NUMBER", "CLASS_OF_CASE",
                   "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "LATERALITY", "HISTOLOGY", "BEHAVIOR", "GRADE",
                   "DIAGNOSTIC_CONFIRMATION", "TUMOR_SIZE", "REGIONAL_NODES_POSITIVE",
                   "REGIONAL_NODES_EXAMINED", "DX_STAGING_PROC_DAYS", "RX_SUMM_DXSTG_PROC", "TNM_CLIN_T",
                   "TNM_CLIN_N", "TNM_CLIN_M", "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                   "TNM_PATH_STAGE_GROUP", "TNM_EDITION_NUMBER", "ANALYTIC_STAGE_GROUP", "CS_METS_AT_DX",
                   "CS_METS_EVAL", "CS_EXTENSION", "CS_TUMOR_SIZEEXT_EVAL", "CS_METS_DX_BONE", "CS_METS_DX_BRAIN",
                   "CS_METS_DX_LIVER", "CS_METS_DX_LUNG", "LYMPH_VASCULAR_INVASION", "CS_SITESPECIFIC_FACTOR_1",
                   "CS_SITESPECIFIC_FACTOR_2", "CS_SITESPECIFIC_FACTOR_3", "CS_SITESPECIFIC_FACTOR_4",
                   "CS_SITESPECIFIC_FACTOR_5", "CS_SITESPECIFIC_FACTOR_6", "CS_SITESPECIFIC_FACTOR_7",
                   "CS_SITESPECIFIC_FACTOR_8", "CS_SITESPECIFIC_FACTOR_9", "CS_SITESPECIFIC_FACTOR_10",
                   "CS_SITESPECIFIC_FACTOR_11", "CS_SITESPECIFIC_FACTOR_12", "CS_SITESPECIFIC_FACTOR_13",
                   "CS_SITESPECIFIC_FACTOR_14", "CS_SITESPECIFIC_FACTOR_15", "CS_SITESPECIFIC_FACTOR_16",
                   "CS_SITESPECIFIC_FACTOR_17", "CS_SITESPECIFIC_FACTOR_18", "CS_SITESPECIFIC_FACTOR_19",
                   "CS_SITESPECIFIC_FACTOR_20", "CS_SITESPECIFIC_FACTOR_21", "CS_SITESPECIFIC_FACTOR_22",
                   "CS_SITESPECIFIC_FACTOR_23", "CS_SITESPECIFIC_FACTOR_24", "CS_SITESPECIFIC_FACTOR_25",
                   "CS_VERSION_LATEST", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS", "DX_DEFSURG_STARTED_DAYS",
                   "RX_SUMM_SURG_PRIM_SITE", "RX_HOSP_SURG_APPR_2010", "RX_SUMM_SURGICAL_MARGINS",
                   "RX_SUMM_SCOPE_REG_LN_SUR", "RX_SUMM_SURG_OTH_REGDIS", "SURG_DISCHARGE_DAYS", "READM_HOSP_30_DAYS",
                   "REASON_FOR_NO_SURGERY", "DX_RAD_STARTED_DAYS", "RX_SUMM_RADIATION", "RAD_LOCATION_OF_RX",
                   "RAD_TREAT_VOL", "RAD_REGIONAL_RX_MODALITY", "RAD_REGIONAL_DOSE_CGY", "RAD_BOOST_RX_MODALITY",
                   "RAD_BOOST_DOSE_CGY", "RAD_NUM_TREAT_VOL", "RX_SUMM_SURGRAD_SEQ", "RAD_ELAPSED_RX_DAYS",
                   "REASON_FOR_NO_RADIATION", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", "RX_SUMM_CHEMO",
                   "DX_HORMONE_STARTED_DAYS", "RX_SUMM_HORMONE", "DX_IMMUNO_STARTED_DAYS", "RX_SUMM_IMMUNOTHERAPY",
                   "RX_SUMM_TRNSPLNT_ENDO", "RX_SUMM_SYSTEMIC_SUR_SEQ", "DX_OTHER_STARTED_DAYS", "RX_SUMM_OTHER",
                   "PALLIATIVE_CARE", "RX_SUMM_TREATMENT_STATUS", "PUF_30_DAY_MORT_CD", "PUF_90_DAY_MORT_CD",
                   "DX_LASTCONTACT_DEATH_MONTHS", "PUF_VITAL_STATUS", "RX_HOSP_SURG_PRIM_SITE", "RX_HOSP_CHEMO",
                   "RX_HOSP_IMMUNOTHERAPY", "RX_HOSP_HORMONE", "RX_HOSP_OTHER", "PUF_MULT_SOURCE", "REFERENCE_DATE_FLAG",
                   "RX_SUMM_SCOPE_REG_LN_2012", "RX_HOSP_DXSTG_PROC", "PALLIATIVE_CARE_HOSP", "TUMOR_SIZE_SUMMARY",
                   "METS_AT_DX_OTHER", "METS_AT_DX_DISTANT_LN", "METS_AT_DX_BONE", "METS_AT_DX_BRAIN",
                   "METS_AT_DX_LIVER", "METS_AT_DX_LUNG", "NO_HSD_QUAR_16", "MED_INC_QUAR_16", "MEDICAID_EXPN_CODE")



#Read in data for each subsite
lip <- read_fwf('NCDBPUF_Lip.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

melanoma <- read_fwf('NCDBPUF_Melanoma.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
                       
skin <- read_fwf('NCDBPUF_OtSkin.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

hodgextr <- read_fwf('NCDBPUF_HodgExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

hodgndal <- read_fwf('NCDBPUF_HodgNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

NHLndal <- read_fwf('NCDBPUF_NHLNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

breast <-  read_fwf('NCDBPUF_Breast.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

vulva <-  read_fwf('NCDBPUF_Vulva.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

vagina <-  read_fwf('NCDBPUF_Vagina.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

penis <-  read_fwf('NCDBPUF_Penis.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

#Combine data for all subsites
dat <- bind_rows(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, 
                 vulva, vagina, penis)

rm(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, vulva, vagina, penis)

prim_site_text <- data_frame(PRIMARY_SITE = 
                                 #lip
                               c("C000", "C001", "C002", "C003", "C004", "C005",
                                 "C006", "C008", "C009",
                                 
                                 #skin/melanoma
                                 "C440", "C441", "C442", "C443", "C444", "C445",
                                 "C446", "C447", "C448", "C449",
                                 
                                 #breast - nipple
                                 "C500",
                                 
                                 #vagina/vulva
                                 "C510", "C511", "C512", "C518", "C519", "C529",
                                 
                                 #penis
                                 "C600", "C601", "C602", "C608", "C609", "C639"
                                 
                                 #NHL sites
                                 "C098", "C099", "C111", "C142", "C379", "C422",
                                 "C770","C771","C772","C773","C774", "C775", 
                                 "C778","C779"
                                 ),                
SITE_TEXT = c(
  #lip 
"C00.0 External Lip: Upper NOS",
"C00.1 External Lip: Lower NOS",
"C00.2  External Lip: NOS",
"C00.3 Lip: Upper Mucosa", 
"C00.4 Lip: Lower Mucosa", 
"C00.5 Lip: Mucosa NOS",
"C00.6 Lip: Commissure", 
"C00.8 Lip: Overlapping", 
"C00.9 Lip, NOS",


 #skin
"C44.0 Skin of lip, NOS",
"C44.1 Eyelid",
"C44.2 External ear",
"C44.3 Skin of ear and unspecified parts of face",
"C44.4 Skin of scalp and neck",
"C44.5 Skin of trunk",
"C44.6 Skin of upper limb and shoulder",
"C44.7 Skin of lower limb and hip",
"C44.8 Overlapping lesion of skin",
"C44.9 Skin, NOS", 

#breast
"C50.0 Nipple",

#vulva/vagina
"C51.0 Labium majus",
"C51.1 Labium minus",
"C51.2 Clitoris",
"C51.8 Overlapping lesion of vulva",
"C51.9 Vulva, NOS",
"C52.9 Vagina, NOS",

#penis
"C60.0 Prepuce",
"C60.1 Glans penis",
"C60.2 Body of penis",
"C60.8 Overlapping lesion of penis",
"C60.9 Penis",
"C63.2 Scrotum, NOS",

#NHL
  "C09.8 Tonsil: Overlapping",
  "C09.9 = Tonsil NOS",
  "C11.1 Nasopharynx: Poster Wall", 
  "C14.2 Waldeyer Ring",
  "C37.9 Thymus",
  "C42.2 Spleen",
  "C77.0 Lymph Nodes: HeadFaceNeck",
  "C77.1 Intrathoracic Lymph Nodes",
  "C77.2 Intra-abdominal LymphNodes",
  "C77.3 Lymph Nodes of axilla or arm ",
  "C77.4 Lymph Nodes: Leg",
  "C77.5 Pelvic Lymph Nodes",
  "C77.8 Lymph Nodes: multiple region",
  "C77.9 Lymph Node NOS"))


dat <- merge(dat, prim_site_text, by = "PRIMARY_SITE", all.x = TRUE) 
 
rm(prim_site_text)

# convert numeric variables from character class to numeric class
num_vars <- c("AGE", "CROWFLY", "TUMOR_SIZE", "DX_STAGING_PROC_DAYS", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
              "DX_DEFSURG_STARTED_DAYS", "SURG_DISCHARGE_DAYS", "DX_RAD_STARTED_DAYS",  "RAD_REGIONAL_DOSE_CGY",
              "RAD_BOOST_DOSE_CGY", "RAD_ELAPSED_RX_DAYS", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", 
              "DX_HORMONE_STARTED_DAYS", "DX_OTHER_STARTED_DAYS", "DX_LASTCONTACT_DEATH_MONTHS",
              "RAD_NUM_TREAT_VOL")

dat[num_vars] <- lapply(dat[num_vars], as.numeric)


# convert factor variables from character class to factor class
vars <- names(dat)
fact_vars <- vars[!(vars %in% num_vars)] # basically all of the non-numerics

dat[fact_vars] <- lapply(dat[fact_vars], as.character)
dat[fact_vars] <- lapply(dat[fact_vars], as.factor)

dat <- dat %>%
        mutate(FACILITY_TYPE_F = fct_recode(FACILITY_TYPE_CD,
                                            "Community Cancer Program" = "1",
                                            "Comprehensive Comm Ca Program" = "2",
                                            "Academic/Research Program" = "3",
                                            "Integrated Network Ca Program" = "4",
                                            "Other" = "9")) %>%
        mutate(FACILITY_LOCATION_F = fct_recode(FACILITY_LOCATION_CD,
                                            "New England" = "1",
                                            "Middle Atlantic" = "2",
                                            "South Atlantic" = "3",
                                            "East North Central" = "4",
                                            "East South Central" = "5",
                                            "West North Central" = "6",
                                            "West South Central" = "7",
                                            "Mountain" = "8",
                                            "Pacific" = "9",
                                            "out of US" = "0")) %>%
        mutate(FACILITY_GEOGRAPHY = fct_collapse(FACILITY_LOCATION_CD,
                                                 "Northeast" = c("1", "2"),
                                                 "South" = c("3", "7"),
                                                 "Midwest" = c("4", "5", "6"),
                                                 "West" = c("8", "9"))) %>%
        mutate(AGE_F = cut(AGE, c(0, 54, 64, 74, 100))) %>%
        mutate(AGE_40 = cut(AGE, c(0, 40, 100))) %>%
        mutate(SEX_F = fct_recode(SEX,
                                "Male" = "1",
                                "Female" = "2")) %>%
        mutate(RACE_F = fct_collapse(RACE,
                                "White" = c("01"),
                                "Black" = c("02"),
                                "Asian" = c("04", "05", "06", "07", "08", "10", "11", "12", "13", "14", "15",
                                            "16", "17", "20", "21", "22", "25", "26", "27", "28", "30", "31",
                                            "32", "96", "97"),
                                "Other/Unk" = c("03", "98", "99"))) %>%
        mutate(HISPANIC = fct_collapse(SPANISH_HISPANIC_ORIGIN,
                                       "Yes" = c("1", "2", "3", "4", "5", "6", "7", "8"),
                                       "No" = c("0"),
                                       "Unknown" = c("9"))) %>%
        mutate(INSURANCE_F = fct_recode(INSURANCE_STATUS,
                                         "None" = "0",
                                         "Private" = "1",
                                         "Medicaid" = "2",
                                         "Medicare" = "3",
                                         "Other Government" = "4",
                                         "Unknown" = "9")) %>%
        mutate(INSURANCE_F = fct_relevel(INSURANCE_F,
                                         "Private")) %>%
        mutate(INCOME_F = fct_recode(MED_INC_QUAR_12,
                                      "Less than $38,000" = "1",
                                      "$38,000 - $47,999" = "2",
                                      "$48,000 - $62,999" = "3",
                                      "$63,000 +" = "4")) %>%
        mutate(EDUCATION_F = fct_recode(NO_HSD_QUAR_12,
                                        "21% or more" = "1",
                                        "13 - 20.9%" = "2",
                                        "7 - 12.9%" = "3",
                                        "Less than 7%" = "4")) %>%
        mutate(U_R_F = fct_collapse(UR_CD_13,
                                    "Metro" = c("1", "2", "3"),
                                    "Urban" = c("4", "5", "6", "7"),
                                    "Rural" = c("8", "9"))) %>%
        mutate(CLASS_OF_CASE_F = fct_collapse(CLASS_OF_CASE,
                                              All_Part_Prim = c("10", "11", "12", "13",
                                                                "14", "20", "21", "22"),
                                              Other_Facility = c("00"))) %>%
        mutate(GRADE_F = fct_recode(GRADE,
                                  "Gr I: Well Diff" = "1",
                                  "Gr II: Mod Diff" = "2",
                                  "Gr III: Poor Diff" = "3",
                                  "Gr IV: Undiff/Anaplastic" = "4",
                                  "NA/Unkown" = "9")) %>%
        mutate(HISTOLOGY_F = fct_infreq(HISTOLOGY)) %>%
        mutate(HISTOLOGY_F = factor(HISTOLOGY_F)) %>%
        mutate(HISTOLOGY_F_LIM = fct_lump(HISTOLOGY_F, prop = 0.05)) %>%
        mutate(TNM_CLIN_T = fct_recode(TNM_CLIN_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_T = fct_relevel(TNM_CLIN_T,
                                        "1")) %>%
        mutate(TNM_CLIN_N = fct_recode(TNM_CLIN_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_M = fct_recode(TNM_CLIN_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_recode(TNM_PATH_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_relevel(TNM_PATH_T,
                                        "1")) %>%
        mutate(TNM_PATH_N = fct_recode(TNM_PATH_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_M = fct_recode(TNM_PATH_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_STAGE_GROUP = fct_recode(TNM_CLIN_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_STAGE_GROUP = fct_recode(TNM_PATH_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(MARGINS = fct_recode(RX_SUMM_SURGICAL_MARGINS,
                                    "No Residual" = "0",
                                    "Residual, NOS" = "1",
                                    "Microscopic Resid" = "2",
                                    "Macroscopic Resid" = "3",
                                    "Not evaluable" = "7",
                                    "No surg" = "8",
                                    "Unknown" = "9")) %>%
        mutate(MARGINS_YN = fct_collapse(RX_SUMM_SURGICAL_MARGINS,
                                         "Yes" = c("1", "2", "3"),
                                         "No" = c("0"),
                                         "No surg/Unk/NA" = c("7", "8", "9"))) %>%
        mutate(READM_HOSP_30_DAYS_F = fct_recode(READM_HOSP_30_DAYS,
                                                 "No_Surg_or_No_Readmit" = "0",
                                                 "Unplan_Readmit_Same" = "1",
                                                 "Plan_Readmit_Same" = "2",
                                                 "PlanUnplan_Same" = "3",
                                                 "Unknown" = "4")) %>%
        mutate(RX_SUMM_RADIATION_F = fct_recode(RX_SUMM_RADIATION,
                                                "None" = "0",
                                                "Beam Radiation" = "1",
                                                "Radioactive Implants" = "2",
                                                "Radioisotopes" = "3",
                                                "Beam + Imp or Isotopes" = "4",
                                                "Radiation, NOS" = "5",
                                                "Unknown" = "9")) %>%
        mutate(PUF_30_DAY_MORT_CD_F = fct_recode(PUF_30_DAY_MORT_CD,
                                                 "Alive_30" = "0",
                                                 "Dead_30" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(PUF_90_DAY_MORT_CD_F = fct_recode(PUF_90_DAY_MORT_CD,
                                                 "Alive_90" = "0",
                                                 "Dead_90" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(LYMPH_VASCULAR_INVASION_F = fct_recode(LYMPH_VASCULAR_INVASION,
                                                      "Neg_LymphVasc_Inv" = "0",
                                                      "Pos_LumphVasc_Inv" = "1",
                                                      "N_A" = "8",
                                                      "Unknown" = "9")) %>%
        mutate(RX_HOSP_SURG_APPR_2010_F = fct_recode(RX_HOSP_SURG_APPR_2010,
                                                     "No_Surg" = "0",
                                                     "Robot_Assist" = "1",
                                                     "Robot_to_Open" = "2",
                                                     "Endo_Lap" = "3",
                                                     "Endo_Lap_to_Open" = "4",
                                                     "Open_Unknown" = "5",
                                                     "Unknown" = "9")) %>%
        mutate(All = "All") %>%
        mutate(All = factor(All)) %>%
        mutate(REASON_FOR_NO_SURGERY_F = fct_recode(REASON_FOR_NO_SURGERY,
                                                    "Surg performed" = "0",
                                                    "Surg not recommended" = "1",
                                                    "No surg due to pt factors" = "2",
                                                    "No surg, pt died" = "5",
                                                    "Surg rec, not done" = "6",
                                                    "Surg rec, pt refused" = "7",
                                                    "Surg rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(SURGERY_YN = ifelse(REASON_FOR_NO_SURGERY == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_SURGERY == "9",
                                          "Ukn",
                                          "No"))) %>%
        mutate(SURG_TF = case_when(SURGERY_YN == "Yes" ~ TRUE,
                             SURGERY_YN == "No" ~ FALSE,
                             TRUE ~ NA))  %>%
        mutate(REASON_FOR_NO_RADIATION_F = fct_recode(REASON_FOR_NO_RADIATION,
                                                    "Rad performed" = "0",
                                                    "Rad not recommended" = "1",
                                                    "No Rad due to pt factors" = "2",
                                                    "No Rad, pt died" = "5",
                                                    "Rad rec, not done" = "6",
                                                    "Rad rec, pt refused" = "7",
                                                    "Rad rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(RADIATION_YN = ifelse(REASON_FOR_NO_RADIATION == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_RADIATION == "9",
                                          NA,
                                          "No"))) %>%
        mutate(SURGRAD_SEQ_F = fct_recode(RX_SUMM_SURGRAD_SEQ,
                                                   "None or Surg or Rad" = "0",
                                                   "Rad before Surg" = "2",
                                                   "Surg before Rad" = "3",
                                                   "Rad before and after Surg" = "4",
                                                   "Intraop Rad" = "5",
                                                   "Intraop Rad plus other" = "6",
                                                   "Unknown" = "9")) %>%
        mutate(SURG_RAD_SEQ = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                     "Surg Alone",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                            "Rad Alone",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0",
                                                   "No Treatment",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2",
                                                          "Rad then Surg",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3",
                                                                 "Surg then Rad",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4",
                                                                        "Rad before and after Surg",
                                                                        "Other"))))))) %>%
        mutate(SURG_RAD_SEQ = fct_relevel(SURG_RAD_SEQ,
                                          "Surg Alone",
                                          "Surg then Rad",
                                          "Rad Alone")) %>%
        mutate(CHEMO_YN = fct_collapse(RX_SUMM_CHEMO,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01", "02", "03"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(SURG_RAD_SEQ_C = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                     "Surg, No rad, No Chemo",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                            "Rad, No Surg, No Chemo",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                                   "No Surg, No Rad, No Chemo",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "No",
                                                          "Rad then Surg, No Chemo",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "No",
                                                                 "Surg then Rad, No Chemo",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "No",
                                                                        "Rad before and after Surg, No Chemo",
                                ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                       "Surg, No rad, Yes Chemo",
                                       ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                              "Rad, No Surg, Yes Chemo",
                                              ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                                     "No Surg, No Rad, Yes Chemo",
                                                     ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "Yes",
                                                            "Rad then Surg, Yes Chemo",
                                                            ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "Yes",
                                                                   "Surg then Rad, Yes Chemo",
                                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "Yes",
                                                                          "Rad before and after Surg, Yes Chemo",
                                                                          "Other"))))))))))))) %>%
        mutate(SURG_RAD_SEQ_C = fct_infreq(SURG_RAD_SEQ_C)) %>%
        mutate(T_SIZE = as.numeric(TUMOR_SIZE)) %>%
        mutate(T_SIZE = ifelse(T_SIZE == 0,
                                "No Tumor",
                                ifelse(T_SIZE > 0 & T_SIZE < 10 | T_SIZE == 991,
                                       "< 1 cm",
                                       ifelse(T_SIZE >= 10 & T_SIZE < 20 | T_SIZE == 992,
                                              "1-2 cm",
                                              ifelse(T_SIZE >= 20 & T_SIZE < 30 | T_SIZE == 993,
                                                     "2-3 cm",
                                                     ifelse(T_SIZE >= 30 & T_SIZE < 40 | T_SIZE == 994,
                                                            "3-4 cm",
                                                            ifelse(T_SIZE >= 40 & T_SIZE < 50 | T_SIZE == 995,
                                                                   "4-5 cm",
                                                                   ifelse(T_SIZE >= 50 & T_SIZE < 60 | T_SIZE == 996,
                                                                          "5-6 cm",
                                                                          ifelse(T_SIZE >= 60 & T_SIZE <= 987 |
                                                                                         T_SIZE == 980 | T_SIZE == 989 |
                                                                                         T_SIZE == 997,
                                                                          ">6 cm",
                                                                          ifelse(T_SIZE == 988 | T_SIZE == 999,
                                                                                 "NA_unk",
                                                                                 "Microscopic focus")))))))))) %>%
        mutate(T_SIZE = factor(T_SIZE)) %>%
        mutate(T_SIZE = fct_relevel(T_SIZE,
                                     "No Tumor", "Microscopic focus", "< 1 cm", "1-2 cm", "2-3 cm", "3-4 cm",
                                       "4-5 cm", "5-6 cm", ">6 cm", "NA_unk")) %>%
        mutate(mets_at_dx = case_when(CS_METS_DX_LUNG == "1" ~ "Lung",
                                      CS_METS_DX_BONE == "1" ~ "Bone",
                                      CS_METS_DX_BRAIN == "1" ~ "Brain",
                                      CS_METS_DX_LIVER == "1" ~ "Liver",
                                      TRUE ~ "None/Other/Unk/NA")) %>%
        mutate(MEDICAID_EXPN_CODE = fct_recode(MEDICAID_EXPN_CODE,
                                               "Non-Expansion State" = "0",
                                               "Jan 2014 Expansion States" = "1",
                                               "Early Expansion States (2010-13)" = "2",
                                               "Late Expansion States (> Jan 2014)" = "3",
                                               "Suppressed for Ages 0 - 39" = "9"))  %>%
        mutate(EXPN_GROUP =  case_when(MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Post-Expansion",
                                       
                                       MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% 
                                          c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013") ~ "Pre-Expansion",
               
                                       MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2010", "2011", "2012", "2013", "2014", "2015") ~ "Post-Expansion",
                                       
                                        MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", "2009") ~ "Pre-Expansion",

                                       MEDICAID_EXPN_CODE %in% c("Non-Expansion State") ~ "Pre-Expansion",

                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") ~ "Pre-Expansion",
                    
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") & 
                                        YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Exclude",
                                       
                                       MEDICAID_EXPN_CODE == "Suppressed for Ages 0 - 39" ~ "Exclude")) %>%
  
  mutate(pre_2014 = YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013")) %>%
  
  mutate(mets_at_dx_F = ifelse(mets_at_dx == "None/Other/Unk/NA", FALSE, TRUE)) %>% 
  
  mutate(Tx_YN = ifelse(SURG_RAD_SEQ == "No Treatment" & CHEMO_YN == "No", FALSE, 
                        ifelse(CHEMO_YN == "Ukn", NA, 
                               TRUE)))

fact_vars_2 <- c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "AGE_F", "SEX_F", "RACE_F",
                 "HISPANIC", "INSURANCE_F", "INCOME_F", "EDUCATION_F", "U_R_F",
                 "CDCC_TOTAL_BEST", "CLASS_OF_CASE_F", "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "HISTOLOGY",
                 "BEHAVIOR", "GRADE_F", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M", "TNM_PATH_STAGE_GROUP",
                 "MARGINS", "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "mets_at_dx")


dat <- dat %>%
        mutate_at(fact_vars_2, funs(factor(.)))

```



# Extract Data of Interest

```{r}
# Melanoma
site_code <- c(
  #lip  
  "C000", "C001", "C002", "C003", "C004", "C005","C006", "C008","C009",
                                  
                                 
#skin/melanoma
  "C440", "C441", "C442", "C443", "C444", "C445", "C446", "C447", "C448", "C449",
                                 
 #breast - nipple
  "C500",
                                 
#vagina/vulva
  "C510", "C511", "C512", "C518", "C519", "C529",
                                 
#penis
 "C600", "C601", "C602", "C608", "C609", "C639")


histo_code <- c("8720", "8741", "8746", "8721", "8722", "8723", "8730", "8740", "8742", "8743", "8744", "8745", "8761")

behavior_code <- c("3")

data <- dat %>%
        filter(BEHAVIOR %in% behavior_code) %>%
        filter(PRIMARY_SITE %in% site_code) %>%
        filter(HISTOLOGY %in% histo_code) %>%
        filter(is.na(PUF_VITAL_STATUS) == FALSE) %>%
        filter(SEQUENCE_NUMBER == "00") %>%
        filter(is.na(DX_LASTCONTACT_DEATH_MONTHS) == FALSE)
   # filter(AGE >= 18) %>%
   #      filter(TNM_CLIN_M %in% c("c0")) %>%
   #      
   #      filter(CLASS_OF_CASE %in% c("10", "12", "14", "22")) %>%
        

no_Excludes <- as.data.frame(data %>% 
                               filter(EXPN_GROUP != "Exclude") 
                             %>% droplevels())


file_path <- c("/Users/beastatlife/Google Drive/Penn/Research/Barbieri/NCDB")
save(data,
      file = paste0(file_path, "/melanoma_data.Rda"))
```

```{r loadData}
#load("melanoma_data.Rda")
```




Data including skin tumors was obtained from the NCBD on October 7, 2019. Cases that were included in this analysis were those with:

1. Site codes: `r site_code`
2. Histology codes: `r histo_code`
3. Behavior codes: `r behavior_code`


Patients were excluded if they didn't have values for either follow up or vital status.

Patients were excluded if they had surgery to a distant site using `RX_SUMM_SURG_OTH_REGDIS`. This was done to avoid confounding of different surgical procedures. We are only interested in surgery at the primary site. These distant site surgeries were being counted in the surgery/radiation sequence and thus to simplify analysis they were removed. 

```{r}

data %>%
        CreateTableOne(data = .,
                     vars = c("RX_SUMM_SURG_OTH_REGDIS"),
                     includeNA = TRUE) %>%
        print(.,
              showAllLevels = TRUE)

data <- data %>%
        filter(RX_SUMM_SURG_OTH_REGDIS == "0") 
```


Race was grouped as white, black, asian, other/unknown
Stage was grouped into 0, I, II, III, IV, NA_Unknown, stage 0 was removed
Whether surgery was performed was based on the `REASON_FOR_NO_SURGERY` variable. The `SURGERY_YN` variable was classified as 'Yes', 'No', or 'Unknown'.


Whether radiation was performed was based on the `REASON_FOR_NO_RADIATION` variable. The `RADIATION_YN` variable was classified as 'Yes', 'No', or 'Unknown'.



#Table of variables for all cases:

```{r}

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "HISTOLOGY_F_LIM", "HISTOLOGY_F", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE", "EXPN_GROUP", "SITE_TEXT"))



p_table(no_Excludes,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "HISTOLOGY_F_LIM", "HISTOLOGY_F", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE","SITE_TEXT"), 
        strata = "EXPN_GROUP")


p_table(data,
        vars = c("YEAR_OF_DIAGNOSIS"),
        strata = c("MEDICAID_EXPN_CODE"))
```


```{r}

preExpMedicare  <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion" & INSURANCE_F == "Medicare"))
postExpMedicare <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion" & INSURANCE_F == "Medicare"))

# p = 0.25 when comparing change in proportion of patients with Medicare before and after ACA expansion
prop.test(c(preExpMedicare, postExpMedicare), 
          c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))

preExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion") %>% 
                            filter(INSURANCE_F == "None"))
postExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion") %>% 
                             filter(INSURANCE_F == "None"))

# Significant decrease in the overall proportion of patients without insurance after ACA expansion 
prop.test(c(preExpNoInsurance, postExpNoInsurance), 
          c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))

p_table(no_Excludes, strata = "EXPN_GROUP", vars = "DX_RX_STARTED_DAYS")

data <- data %>% mutate(Insured = INSURANCE_F != "Unknown")

```




#Kaplan Meier Analysis


##All

```{r}
uni_var(test_var = "All", data_imp = data)
```

##Facility Type
```{r}
uni_var(test_var = "FACILITY_TYPE_F", data_imp = data)
```

##Facility Location

```{r}
uni_var(test_var = "FACILITY_LOCATION_F", data_imp = data)
```

##Facility Geography

```{r}
uni_var(test_var = "FACILITY_GEOGRAPHY", data_imp = data)
```

##Age Group

```{r}
uni_var(test_var = "AGE_F", data_imp = data)
```

##Age Group
```{r}
uni_var(test_var = "AGE_40", data_imp = data)
```

##Gender

```{r}
uni_var(test_var = "SEX_F", data_imp = data)
```

##RACE_F

```{r}
uni_var(test_var = "RACE_F", data_imp = data)
```

##Hispanic

```{r}
uni_var(test_var = "HISPANIC", data_imp = data)
```

##Insurance Status

```{r}
uni_var(test_var = "INSURANCE_F", data_imp = data)
```

##Overall Survival pre/post-ACA expansion

```{r}
uni_var(test_var = "EXPN_GROUP", data_imp = no_Excludes)
```


<!-- ##Income -->

<!-- ```{r} -->
<!-- class(data$INCOME_F) -->
<!-- uni_var(test_var = "INCOME_F", data_imp = data) -->
<!-- ``` -->

##Education

```{r}
uni_var(test_var = "EDUCATION_F", data_imp = data)
```

##Urban/Rural

```{r}
uni_var(test_var = "U_R_F", data_imp = data)
```

##Class (treatment at performing facility)

```{r}
uni_var(test_var = "CLASS_OF_CASE_F", data_imp = data)
```

##Year

```{r}
uni_var(test_var = "YEAR_OF_DIAGNOSIS", data_imp = data)
```

##Primary Site

```{r}
uni_var(test_var = "SITE_TEXT", data_imp = data)
```


##Histology

```{r}
uni_var(test_var = "HISTOLOGY_F_LIM", data_imp = data)
```

<!-- ##Behavior -->

<!-- ```{r} -->
<!-- uni_var(test_var = "BEHAVIOR", data_imp = data) -->
<!-- ``` -->

##Grade

```{r}
uni_var(test_var = "GRADE_F", data_imp = data)
```

##Clinical T Stage

```{r}
uni_var(test_var = "TNM_CLIN_T", data_imp = data)
```

##Clinical N Stage

```{r}
uni_var(test_var = "TNM_CLIN_N", data_imp = data)
```

<!-- ##Clinical M Stage -->

<!-- ```{r} -->
<!-- uni_var(test_var = "TNM_CLIN_M", data_imp = data) -->
<!-- ``` -->

##Clinical Stage Group

```{r}
uni_var(test_var = "TNM_CLIN_STAGE_GROUP", data_imp = data)
```

##Pathologic T Stage

```{r}
uni_var(test_var = "TNM_PATH_T", data_imp = data)
```

##Pathologic N Stage

```{r}
uni_var(test_var = "TNM_PATH_N", data_imp = data)
```

##Pathologic M Stage

```{r}
uni_var(test_var = "TNM_PATH_M", data_imp = data)
```

##Pathologic Stage Group

```{r}
uni_var(test_var = "TNM_PATH_STAGE_GROUP", data_imp = data)
```

##Margins
```{r}
uni_var(test_var = "MARGINS", data_imp = data)
```

##Margins Yes/No
```{r}
#uni_var(test_var = "MARGINS_YN", data_imp = data)
```

##30 Day Readmission

```{r}
uni_var(test_var = "READM_HOSP_30_DAYS_F", data_imp = data)
```

##Radiation Type

```{r}
uni_var(test_var = "RX_SUMM_RADIATION_F", data_imp = data)
```


##Lymphovascular Invasion

```{r}
uni_var(test_var = "LYMPH_VASCULAR_INVASION_F", data_imp = data)
```

##Endoscopic/Robotic

```{r}
uni_var(test_var = "RX_HOSP_SURG_APPR_2010_F", data_imp = data)
```

##Surgery Radiation Sequence 

```{r}
uni_var(test_var = "SURG_RAD_SEQ", data_imp = data)
```

##Surgery Yes/No

```{r}
uni_var(test_var = "SURGERY_YN", data_imp = data)
```

##Radiation Yes/No

```{r}
uni_var(test_var = "RADIATION_YN", data_imp = data)
```

##Chemo Yes/No

```{r}
uni_var(test_var = "CHEMO_YN", data_imp = data)
```


##Treatment Yes/No
```{r}
uni_var(test_var = "Tx_YN", data_imp = data)
```

##Metastases at Dx
```{r}
uni_var(test_var = "mets_at_dx_F", data_imp = data)
```

<!-- ##Tumor Size -->

<!-- ```{r} -->
<!-- uni_var(test_var = "T_SIZE", data_imp = data) -->
<!-- ``` -->

#Tumor specific Variables


###Node Size


#Cox Proportional Hazard Ratio

##Model #1

###Full analysis

```{r}
model_one <- coxph(Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)
                     ~ SURG_RAD_SEQ + INSURANCE_F + AGE + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F,
                     data = data)
model_one %>% summary()


```

###Summary of Model

```{r}
model_one %>%
        tidy(., exponentiate = TRUE) %>%
        select(term, estimate, conf.low, conf.high, p.value) %>%
        rename(Variable = term,
               Hazard_Ratio = estimate) %>%
        tbl_df %>%
        print(n = nrow(.))

```

# Prediction Logistic Regression Models

## Surgery
```{r}

no_Ukns <- data %>%
  filter(SURGERY_YN != "Ukn") %>% 
  droplevels() %>% 
  mutate(SURGERY_YN = as.logical(SURGERY_YN))

fit_surg <- glm(SURG_TF ~ AGE_F + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP,
   data = no_Ukns)

summary(fit_surg)

exp(cbind("Odds ratio" = coef(fit_surg), confint.default(fit_surg, level = 0.95)))
```

## Metastasis at Time of Diagnosis
```{r}
# limit to those cases where data about expansion status is available (> Age 39, non-ambiguous status states)

fit_mets <- glm(mets_at_dx_F ~ AGE_F + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP,
   data = no_Excludes)

summary(fit_mets)

exp(cbind("Odds ratio" = coef(fit_mets), confint.default(fit_surg, level = 0.95)))
```
